Bioengineering

11 pages, 1365 KB

Open AccessArticle

Comparison of CO₂ Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis

by Kadylova Yerkezhan, Nazym S. Sagandykova, Madina Baurzhan, Aigerim Mashekova, Bekpan Almat, Autalipov Darkhan, Olzhas Mukhmetov, Damir Abdrakhmanov, Eddie Yin Kwee Ng and Sayagul Kairgeldina

Bioengineering 2026, 13(6), 713; https://doi.org/10.3390/bioengineering13060713 - 22 Jun 2026

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Abstract

Background. Recurrent respiratory papillomatosis (RRP) in children remains a pressing issue in pediatric otolaryngology, characterized by a chronic course, frequent relapses, and the need for repeat surgical interventions. The aim of this study was to evaluate whether the surgical technique used for [...] Read more.

Background. Recurrent respiratory papillomatosis (RRP) in children remains a pressing issue in pediatric otolaryngology, characterized by a chronic course, frequent relapses, and the need for repeat surgical interventions. The aim of this study was to evaluate whether the surgical technique used for primary removal of pediatric RRP—CO₂ laser, microdebrider, or a combined approach—was associated with clinically documented recurrence and early recurrence within 12 months. Materials and Methods. A retrospective study of 53 medical records of children who underwent their first surgery for RRP between 2019 and 2023 was conducted. Three surgical approaches were used: CO₂ laser, microdebrider, and a combined method. Statistical analysis was performed using Pearson’s χ² test, and the strength of association was evaluated with Cramér’s V. Results. The most frequently used method was the CO₂ laser (n = 25), followed by a microdebrider (n = 16), and a combined method (n = 12). During the observation period, disease recurrence was recorded in 35 of 53 patients (66.0%): in 20 children, within the first 12 months after surgery, and in 15, after more than 12 months. No recurrence was documented in the available medical records for 18 patients during the observation period. No statistically significant effect of the surgical treatment method on the recurrence rate (p = 0.813) or the risk of early recurrence (p = 0.926) was found. Also, no significant association was found between the child’s age and either the overall recurrence rate (p = 0.510) or the likelihood of early recurrence (p = 0.217). Conclusions. Within the limitations of this retrospective single-center study, neither the surgical treatment method nor the patient’s age was associated with clinically documented recurrence or early recurrence recorded in the available medical records. Full article

(This article belongs to the Special Issue AI and Data Science in Biomedicine: Powering the Next Generation of Diagnostics and Therapies)

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17 pages, 3073 KB

Open AccessArticle

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues

by Katherine L. Hebert, Jonathan J. Savoie, Mackenzie L. Hawes, Britney Nguyen, Madison Lee, Marcus A. Moody, Sophie R. Dietrich, Thomas Cheng, Van H. Barnes, Bridgette M. Collins-Burow, Alison A. Smith, Frank H. Lau, W. Todd Monroe, Matthew E. Burow, Elizabeth C. Martin and Jorge A. Belgodere

Bioengineering 2026, 13(6), 712; https://doi.org/10.3390/bioengineering13060712 - 21 Jun 2026

Viewed by 419

Abstract

Current 3D tumor models for aggressive breast cancers inadequately recapitulate the native tumor microenvironment (TME), leading to poor translational potential. There is a critical need for models capable of mimicking the unique biochemical signals present in the TME. To address this gap, breast [...] Read more.

Current 3D tumor models for aggressive breast cancers inadequately recapitulate the native tumor microenvironment (TME), leading to poor translational potential. There is a critical need for models capable of mimicking the unique biochemical signals present in the TME. To address this gap, breast tissue and a patient-derived xenograft tumor were decellularized and processed to produce breast tissue- and tumor-specific decellularized extracellular matrices (dECM). Histology confirmed complete cellular removal while maintaining the ECM. Further, DNA content was significantly reduced while ECM composition (POSTN, COLI, FN1) was retained. Breast dECM was incorporated (0, 5, 10, 20, and 50 µg/mL) with triple-negative breast cancer cell lines to generate spheroids. Imaging and histology demonstrated that cells in low dECM (5 and 10 µg/mL) formed compact singular spheres, while higher dECM concentrations (20 and 50 µg/mL) resulted in cells concentrated on the outer edge of the sphere and irregular sphere circularity. RNA-sequencing of MDA-MB-231 dECM spheres demonstrated that gene changes were mediated by both the inclusion of dECM and its composition. High-density tumor dECM upregulated genes associated with metastasis, while high-density breast dECM enhanced tumor suppressors and anti-metastasis genes. These findings indicate that dECM provides physiological cues in 3D tumor models by incorporating TME. Full article

(This article belongs to the Special Issue Extracellular Matrix Biomaterial for Tissue Engineering and Regenerative Medicine, 2nd Edition)

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28 pages, 3012 KB

Open AccessArticle

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters—A Pilot Study

by Emilio Manuel Arrayales-Millán, Miguel Rodal, Mirvana Elizabeth González-Macías, Carlos Villa-Angulo, Karla Raquel Keys-González, Arnulfo Ramos-Jiménez, Isabella Arrayales-Mejia and Kostantinos Gianikellis

Bioengineering 2026, 13(6), 711; https://doi.org/10.3390/bioengineering13060711 - 21 Jun 2026

Viewed by 271

Abstract

This study investigated balance control during the half squat by analyzing the relationship between the center of mass (CoM) and the center of pressure (CoP) in five experienced male weightlifters performing segmented squats at five load levels (20–80% 1 RM) across four Power-Based [...] Read more.

This study investigated balance control during the half squat by analyzing the relationship between the center of mass (CoM) and the center of pressure (CoP) in five experienced male weightlifters performing segmented squats at five load levels (20–80% 1 RM) across four Power-Based Training (PBT) exercises. The area of the 95% confidence ellipse was quantified using the Vicon motion capture system in conjunction with AMTI force plates. Given the small sample size (n = 5), a dual inference approach was implemented—frequentist repeated-measures analysis of variance (ANOVA) complemented by a unified adaptive Bayesian hierarchical model—to mitigate Type II error in low-power scenarios. Regarding the movement phase, a marked effect on center of pressure (CoP) stability was observed, as evidenced by both statistical approaches (frequentist: F(1.65, 6.59) = 19.44, p = 0.002, ηp² = 0.829; Bayesian: P(β_phase < 0) > 0.999). Although external load did not reach statistical significance in the frequentist analysis (p = 0.177, achieved power = 0.27), the Bayesian model provided moderate evidence of a positive impact (β_load = 0.059, 95% HDI [0.005, 0.115], p = 0.981). The area of the center of mass (CoM) ellipse showed no effects of interest. Limb asymmetries were significant and consistent throughout the experiment (frequentist: 48.01 ± 30.13%; Bayesian: 69.48%, 95% HDI [55.86%, 81.44%], P(AI > 20%) = 1.000) and were not modulated by the experimental condition. CoP-CoM coupling was stronger in the mediolateral direction than in the anteroposterior direction. The findings reveal that phase is the primary factor in postural stability, exerting a modest positive influence discernible only through low-powered probabilistic inference, and that the dual framework strengthens inferential robustness in small-sample biomechanical studies. Confirmatory studies with larger samples are recommended. Full article

(This article belongs to the Special Issue Biomechanics of Physical Exercise)

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22 pages, 2584 KB

Open AccessArticle

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration

by Anja Heselich, Sebastian Mann, Jörg-Ulf Wiegner and Shahram Ghanaati

Bioengineering 2026, 13(6), 710; https://doi.org/10.3390/bioengineering13060710 - 20 Jun 2026

Viewed by 417

Abstract

Methods of ridge preservation following tooth extraction, aiming to maintain alveolar bone volume and support tissue regeneration, have been extensively researched. Continuously, new approaches and materials are being explored in this context. To scientifically evaluate outcomes, the pre-implant situation is usually assessed radiologically, [...] Read more.

Methods of ridge preservation following tooth extraction, aiming to maintain alveolar bone volume and support tissue regeneration, have been extensively researched. Continuously, new approaches and materials are being explored in this context. To scientifically evaluate outcomes, the pre-implant situation is usually assessed radiologically, histologically, and/or clinically. However, the influence of ridge preservation on implant placement itself is rarely examined in depth, and if at all, the focus has been on implant stability or survival rates. Based on the assumption that preoperative radiological assessment, including cone beam computed tomography, provides only an indirect and inherently limited approximation of actual intraoperative bone condition, undetected factors such as insufficient bone density, mechanically unfavorable trabecular structure, or incompletely resorbed residual biomaterial may necessitate a shift of the implant from the preferred position originally occupied by the tooth root. We therefore established a method that evaluates and categorizes implant position in three dimensions based on radiological data post-implantation. Our data, derived from a multicenter randomized clinical trial (RCT), demonstrate that the greatest positional deviations are observed without preservation, whereas the combination of biomaterial and PRF most frequently allowed for central implant placement. The proposed method proves well suited for evaluating the outcome of ridge preservation procedures. The findings demonstrate that both the absence and presence, and further the type, of preservation have a measurable influence on the final implant positioning. Full article

(This article belongs to the Special Issue Medical Imaging: Techniques, Applications, Impact and Innovations)

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14 pages, 4409 KB

Open AccessArticle

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study

by Fırat Oğuz, Handan Göze Oğuz and Sabahattin Bor

Bioengineering 2026, 13(6), 709; https://doi.org/10.3390/bioengineering13060709 - 20 Jun 2026

Viewed by 401

Abstract

Background: Advances in additive manufacturing and CAD/CAM technologies have expanded the use of 3D-printed orthodontic models in digital aligner workflows. Intraoral scanners (IOS) are critical for accurately capturing attachment geometries and dental morphology during these workflows. However, comparative evidence regarding IOS accuracy in [...] Read more.

Background: Advances in additive manufacturing and CAD/CAM technologies have expanded the use of 3D-printed orthodontic models in digital aligner workflows. Intraoral scanners (IOS) are critical for accurately capturing attachment geometries and dental morphology during these workflows. However, comparative evidence regarding IOS accuracy in models with complex orthodontic structures remains limited. Therefore, this study aimed to compare the trueness and precision of five IOS using 3D-printed orthodontic models with attachments. Methods: In this in vitro study, thirty independent single-arch 3D-printed models (either maxillary or mandibular) with orthodontic attachments were scanned twice with each IOS. The Smart Optics Vinyl laboratory scanner served as the reference scanner. Scans were aligned and superimposed in CloudCompare, and root mean square (RMS) deviation values were calculated to evaluate accuracy. Nonparametric Kruskal–Wallis and Dunn tests were applied (α = 0.05). Results: Significant differences were found among scanners for both trueness and precision (p < 0.001). Primescan, TRIOS 3, and iTero element 5D demonstrated comparable trueness (p > 0.05) and outperformed Rapideye MI-1000 (p < 0.001). iTero element 2 plus showed slightly lower accuracy but remained clinically acceptable. Primescan achieved the highest precision, significantly exceeding iTero element 2 plus, iTero element 5D, and Rapideye MI-1000 (p < 0.01). TRIOS 3 also exhibited excellent repeatability, comparable to Primescan (p = 1.000). Conclusions: All intraoral scanners, except Rapideye MI-1000, demonstrated accuracy levels generally considered clinically acceptable for digital orthodontic and additive manufacturing workflows. Primescan, TRIOS 3, and iTero element 5D exhibited similarly high trueness, while Primescan showed the most consistent precision. The ability of these scanners to reproduce fine anatomical details may improve the reliability of 3D-printed orthodontic models and in-office aligner production workflows. Full article

(This article belongs to the Special Issue Advanced 3D-Printed Biomaterials in Dentistry)

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18 pages, 2092 KB

Open AccessArticle

The Impact of a Heated Effleurage and Heated Tapotement Massage on Low-Back Discomfort from a Seat

by Matt M. Mallette, Nathaniel Gur-Arie, Malak Almonjed and Nicola Gerrett

Bioengineering 2026, 13(6), 708; https://doi.org/10.3390/bioengineering13060708 - 20 Jun 2026

Viewed by 429

Abstract

Lower back pain (LBP) is highly prevalent, and while non-pharmacological treatments exist such as heat or massage, they are rarely combined in a convenient manner. We examined the impact of two different heated massage protocols delivered from an automotive seat on LBP within [...] Read more.

Lower back pain (LBP) is highly prevalent, and while non-pharmacological treatments exist such as heat or massage, they are rarely combined in a convenient manner. We examined the impact of two different heated massage protocols delivered from an automotive seat on LBP within typical commute times. Seventeen adults (eight females) with chronic, non-specific LBP (~6/10 initial back pain) evaluated a heated effleurage or heated tapotement massage—in a randomized order—applied to the lower back, upper thighs, and gluteal region while seated. Each visit included a 20 min control followed by a 20 min heated massage (30 min rest between), with thermal and subjective measurements assessed throughout. Before and after each 20 min session, viscoelastic properties of participants’ lower back muscles were assessed with a myotonometer. Heated massage increased skin temperature, thermal sensation and comfort vs. control (p ≤ 0.039). Both heated massage conditions reduced LBP at 10- and 20 min and reduced subjective tightness at 20 min (p ≤ 0.023). Tapotement produced an earlier reduction in tightness at 10 min and had a greater reduction than effleurage at 20 min (p ≤ 0.026). Increased tissue elasticity was observed in the heated tapotement condition (p ≤ 0.031). Seat-based heated massage offers a convenient method to alleviate LBP, potentially from changes to posterior chain tissue properties. Full article

(This article belongs to the Special Issue Applied Biomechanics in Rehabilitation and Ergonomics)

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36 pages, 4092 KB

Open AccessArticle

Functional Profiling in Paralympic Water Polo Using Deep Learning, Stereo Vision, and Phase-Based Kinematic Analysis: A Pilot Study

by Andrea Zanela

Bioengineering 2026, 13(6), 707; https://doi.org/10.3390/bioengineering13060707 - 19 Jun 2026

Viewed by 397

Abstract

Paralympic water polo requires classification systems that reflect sport-specific functional performance under ecologically valid conditions. This pilot study proposes a task-specific kinematic profiling framework for deriving objective, biomechanically interpretable descriptors of residual motor function. Five male national-level water polo athletes—three with eligible motor [...] Read more.

Paralympic water polo requires classification systems that reflect sport-specific functional performance under ecologically valid conditions. This pilot study proposes a task-specific kinematic profiling framework for deriving objective, biomechanically interpretable descriptors of residual motor function. Five male national-level water polo athletes—three with eligible motor impairments and two able-bodied reference participants—performed standardized sport-specific tasks comprising upright floating, vertical propulsion, unilateral passing, non-contested shooting, and contested shooting under physical opposition. Stereoscopic video, OpenPose-based three-dimensional reconstruction, and phase-based analysis were used to extract features and composite indices of postural control, propulsion capacity, upper-limb residual function, and resistance to perturbation. Automatic ball-release detection matched manual frame-level verification in all 128 analyzed ball-related trials. Within the task-specific indices, where higher scores indicate greater functional burden, core values ranged from 0.05–0.15 for upright floating, 0.29–0.68 for combined arm-and-leg vertical propulsion, and 0.040–0.148 for contested shooting across the available subject–side combinations. The profiles showed task- and side-specific differences in stabilization, propulsion, and post-contact motor reorganization. The framework uses pose estimation as a quantitative measurement tool and treats visibility interruptions as functionally meaningful events rather than noise. It is not intended to replace official classification procedures, but to provide transparent and interpretable candidate descriptors for future evidence-based classification research in Paralympic water polo. Full article

(This article belongs to the Special Issue Dynamic Insights: Unveiling the Biomechanics of Sport Through Motion Analysis, 2nd Edition)

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20 pages, 565 KB

Open AccessArticle

Approach of Dental Implants Through the Transfer-Matrix Method

by Rǎzvan Alexandru Mitrea, Mihai-Sorin Tripa, Alexandru Vlad, Iulia-Maria Bărăian, Petre-Corneliu Opriţoiu, Roxana Carmen Cordoş, Carmen-Gabriela Băcilă, Daniela-Corina Jucan, Mihaela Ligia Ungureşan, Liviu Bolunduţ, Dan Pop, Ioana Monica Duncea, Mariana Florica Pop, Honoriu Vălean, Ioan-Aurel Cherecheş, Veronica Mîndrescu, Viorica-Mihaela Suciu and Doina-Iulia Rotaru

Bioengineering 2026, 13(6), 706; https://doi.org/10.3390/bioengineering13060706 - 19 Jun 2026

Viewed by 342

Abstract

Oral health is a very important issue today. This approach presents an original idea: to model the dental implant as a double-articulated buckling bar on an elastic environment. The mandibular bone is considered as the elastic environment. The buckling bar is analyzed using [...] Read more.

Oral health is a very important issue today. This approach presents an original idea: to model the dental implant as a double-articulated buckling bar on an elastic environment. The mandibular bone is considered as the elastic environment. The buckling bar is analyzed using the Transfer-Matrix Method. The risk of buckling is higher for straight bars subjected to axial compression. Therefore, knowing the critical buckling force is very important, especially in the case of dental implants. This study, based on the Transfer-Matrix Method, was carried out in two steps. In the first step, a double-articulated buckling bar on a rigid environment is considered. The second step involves studying the same doubly articulated bar, but with the joint at the lower end resting on an elastic environment. The bone in which the implant is placed is considered as this elastic environment. The Transfer-Matrix Method is easy to implement and provides quick results for problems involving the shape optimization of structural components. This article presents a completely new idea and an original approach to buckling analysis, with applications to dental implants. This work will serve as a foundation for future research involving experimental investigations of dental implants. Full article

(This article belongs to the Special Issue New Tools for Multidisciplinary Treatment in Dentistry, 2nd Edition)

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17 pages, 5021 KB

Open AccessArticle

Insertion Torque Characteristics of the KS 3 Implant in Weak Bone, Standardized Extraction-Socket-like, and Maxillary Sinus Simulation Models: An In Vitro Comparative Study

by Na Ri Seo, Ye-Seul Jung, Dayeon Park, Jisung Kim, Dong-Wook Han and Bongju Kim

Bioengineering 2026, 13(6), 705; https://doi.org/10.3390/bioengineering13060705 - 19 Jun 2026

Viewed by 353

Abstract

Objective: This in vitro study evaluated the insertion torque characteristics of the KS 3 implant compared with the TSIII implant in standardized artificial bone models representing weak bone, extraction-socket-like reduced support, and maxillary sinus simulation conditions. Materials and Methods: A comparative in vitro [...] Read more.

Objective: This in vitro study evaluated the insertion torque characteristics of the KS 3 implant compared with the TSIII implant in standardized artificial bone models representing weak bone, extraction-socket-like reduced support, and maxillary sinus simulation conditions. Materials and Methods: A comparative in vitro study was performed using three models: a weak bone model, a standardized extraction-socket-like reduced-support model, and a maxillary sinus simulation model. Maximum and final insertion torque values were obtained from torque–depth curves. Torque–depth integrals were additionally calculated as exploratory secondary parameters. Statistical analyses were performed using Welch’s t-test and two-way ANOVA where appropriate, and the results were interpreted as exploratory because of the limited sample size. Results: The KS 3 implant showed higher maximum and/or final insertion torque values than the TSIII implant in the weak bone, extraction-socket-like, and maxillary sinus simulation models. In the maxillary sinus model, the torque values showed directional differences according to implant type and residual bone height under the tested fixed undersized drilling protocols for both CAS drilling and bone compaction drilling. Torque–depth integral analysis provided additional information regarding cumulative insertion resistance. Conclusions: Within the limitations of this controlled in vitro study, the KS 3 implant showed higher insertion torque values than the TSIII implant under the tested artificial bone conditions. These findings should be interpreted as in vitro insertion torque data under the tested artificial bone and drilling conditions, not as evidence of clinical superiority. In the maxillary sinus simulation model, the observed torque differences should be interpreted as the combined effect of implant macrodesign and the fixed undersized drilling protocol, rather than as an isolated macrodesign effect. Full article

(This article belongs to the Special Issue Biomaterials and Technology for Oral and Dental Health, 2nd Edition)

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17 pages, 4016 KB

Open AccessArticle

Machine Learning with Multiparametric MRI and Clinical Biomarkers for Noninvasive Renal Interstitial Fibrosis Staging

by Kexin Wang, Tao Zhao, Tao Su, Yizhu Jiang, Lei Jiang, Jianxing Qiu, Shuo Quan, Jiangtao Liu and Rui Wang

Bioengineering 2026, 13(6), 704; https://doi.org/10.3390/bioengineering13060704 - 19 Jun 2026

Viewed by 422

Abstract

Renal interstitial fibrosis (RIF) is currently assessed by invasive biopsy. This prospective study developed and validated a noninvasive random forest model combining multiparametric MRI and clinical biomarkers for identifying severe RIF in 116 patients with biopsy-confirmed renal disease. Quantitative parameters were extracted from [...] Read more.

Renal interstitial fibrosis (RIF) is currently assessed by invasive biopsy. This prospective study developed and validated a noninvasive random forest model combining multiparametric MRI and clinical biomarkers for identifying severe RIF in 116 patients with biopsy-confirmed renal disease. Quantitative parameters were extracted from IVIM, ASL, phase-contrast MRI, T1 mapping, and BOLD sequences. Fibrosis was classified as mild (<25%) or severe (≥25%). In the held-out test set, the random forest model achieved an AUC of 0.89 (95% CI 0.82–0.96), sensitivity of 0.91, and specificity of 0.73, significantly outperforming clinical-only (AUC 0.63), MRI-only (AUC 0.63), and combined LASSO logistic regression (AUC 0.73) benchmarks. The model also demonstrated superior calibration (Brier score 0.154) and net clinical benefit on decision curve analysis. This integrated MRI–clinical model shows promise for noninvasive identification of severe RIF and warrants external prospective validation. Full article

(This article belongs to the Section Biosignal Processing)

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3 pages, 146 KB

Open AccessEditorial

Biomechanics Analysis in Tissue Engineering

by Rui B. Ruben, Marta Carvalho and Olfa Trabelsi

Bioengineering 2026, 13(6), 703; https://doi.org/10.3390/bioengineering13060703 - 19 Jun 2026

Viewed by 362

Abstract

Tissue engineering lies at the crossroads of biology, medicine, and engineering, playing a crucial role in developing advanced treatments for various pathologies [...] Full article

(This article belongs to the Special Issue Biomechanics Analysis in Tissue Engineering)

17 pages, 4776 KB

Open AccessArticle

Effects of Sintering Parameters on the Microstructure and Optical Transmittance of Monolithic 4 mol% Yttria-Partially Stabilized Zirconia

by Taek-Jun Chung, Myung-Joo Kim, Ho-Beom Kwon, Bongju Kim and Young-Jun Lim

Bioengineering 2026, 13(6), 702; https://doi.org/10.3390/bioengineering13060702 - 19 Jun 2026

Viewed by 405

Abstract

High-translucency 4 mol% yttria-partially stabilized zirconia (4Y-PSZ) is widely used for esthetic restorations, but sintering conditions that balance translucency and microstructural control remain unclear. This study evaluated the independent effects of peak temperature, holding time, and heating rate on the microstructure and total [...] Read more.

High-translucency 4 mol% yttria-partially stabilized zirconia (4Y-PSZ) is widely used for esthetic restorations, but sintering conditions that balance translucency and microstructural control remain unclear. This study evaluated the independent effects of peak temperature, holding time, and heating rate on the microstructure and total luminous transmittance of monolithic 4Y-PSZ. Disks were sintered at peak temperatures of 1470–1560 °C, holding times of 30–180 min, and heating rates of 3–10 °C/min. Grain size and internal defect density (≥0.5 µm) were quantified by scanning electron microscopy, and total luminous transmittance at 0.5 mm thickness was measured using a spectrophotometer. Higher peak temperatures and longer holding times increased grain size (0.481 ± 0.020 to 0.785 ± 0.035 µm, and 0.503 ± 0.037 to 0.730 ± 0.041 µm, respectively) and reduced defect density, whereas heating rate had no significant effect on either. Transmittance remained within a narrow range (approximately 40–43% at 0.5 mm) across all schedules, with 1560 °C yielding the lowest value. These findings indicate that the microstructure of monolithic 4Y-PSZ is governed primarily by peak temperature and holding time, while transmittance is relatively insensitive to the sintering schedule. Practically, a peak temperature of 1500–1530 °C with a 1–2 h hold provides a robust processing window balancing densification, grain coarsening, and optical performance for clinical workflows. Full article

(This article belongs to the Special Issue Dental Implant Reconstruction and Biomechanical Evaluation, 3rd Edition)

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21 pages, 4700 KB

Open AccessArticle

A Compositional Calibration Framework for Multi-Channel Functional Electrical Stimulation Enabling Hand Gesture Generation

by Elena Stefanel, Nicolò Landra, Andrea Prestia, Fabio Rossi, Andrea Mongardi, Paolo Motto Ros and Danilo Demarchi

Bioengineering 2026, 13(6), 701; https://doi.org/10.3390/bioengineering13060701 - 18 Jun 2026

Viewed by 470

Abstract

The application of functional electrical stimulation (FES) to restore hand motor function remains challenging due to the difficulty of calibrating multi-channel stimulation to produce coordinated finger movements. This study proposes a compositional FES calibration framework to customize the stimulation of isolated finger actions [...] Read more.

The application of functional electrical stimulation (FES) to restore hand motor function remains challenging due to the difficulty of calibrating multi-channel stimulation to produce coordinated finger movements. This study proposes a compositional FES calibration framework to customize the stimulation of isolated finger actions and enable their combination into functional hand gestures. The proposed method was validated through a two-session experimental study involving thirteen participants. In the first session, subject-specific stimulation sites and parameters were identified for eight individual finger movements using a structured spatial grid defined over the forearm. The second session, conducted on a subset of five participants, evaluated the generation of seven hand gestures derived from combinations of the isolated movements. Results showed that ten of the thirteen participants achieved at least six movements, while three participants successfully elicited all targeted motions. Successfully elicited movements were generally well isolated, although thumb and ring/little finger extensions proved more difficult to isolate. The second session demonstrated that individually calibrated finger activations can be combined to produce coordinated multi-finger movement patterns, with average finger excursions matching the expected motions. Overall, these preliminary results support the use of compositional calibration strategies to achieve functional multi-finger control with multi-channel FES. Full article

(This article belongs to the Section Biosignal Processing)

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22 pages, 2005 KB

Open AccessArticle

by Tara M. Tabak, Joyce van Sluis, Floris H. P. van Velden, Lioe-Fee. F. de Geus-Oei, Françoise J. Siepel and Riemer H. J. A. Slart

Bioengineering 2026, 13(6), 700; https://doi.org/10.3390/bioengineering13060700 - 18 Jun 2026

Viewed by 660

Abstract

This retrospective quantitative data analysis study aimed to investigate sex- and age-related differences in the physiological distribution of [¹⁸F]FDOPA uptake in long axial field-of-view (LAFOV) PET images across a range of organs and tissues. A retrospective quantitative data analysis study of [...] Read more.

This retrospective quantitative data analysis study aimed to investigate sex- and age-related differences in the physiological distribution of [¹⁸F]FDOPA uptake in long axial field-of-view (LAFOV) PET images across a range of organs and tissues. A retrospective quantitative data analysis study of 106 anonymized PET/CT images acquired from vertex to mid-thigh with minimal abnormalities, divided in two gender groups and two age groups was used for this study. The mean and max lean body mass weighted standardized uptake values (

S U L_{m e a n}

,

S U L_{m a x}

), target-to-background ratios (

T B R

), and coefficients of variation (

C o V

) were used to quantify tracer uptake. Sex- and age-related differences in uptake were organ- and metric-specific. Most organs showed comparable uptake between males and females. However, males exhibited higher absolute uptake in metabolically active organs and females showed greater intra-organ heterogeneity. Aging was generally associated with increased tracer uptake and variability, especially in women, with the hip showing higher uptake in younger individuals. Statistically significant differences were most prominent in women and varied by organ and metric. In conclusion, both sex and age significantly influence [¹⁸F]FDOPA PET tracer uptake and variability in an organ- and metric-specific manner. Incorporating sex- and age-adjusted reference values may improve the accuracy and personalization of PET imaging in clinical and research settings. Full article

(This article belongs to the Section Biosignal Processing)

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15 pages, 695 KB

Open AccessReview

Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions

by Jiangfan Yu, Sibusiso Mdletshe, Hamid Abbasi, Eryn Kwon, Samantha Holdsworth and Alan Wang

Bioengineering 2026, 13(6), 699; https://doi.org/10.3390/bioengineering13060699 - 18 Jun 2026

Viewed by 435

Abstract

Structural magnetic resonance imaging (sMRI) is progressively used to diagnose brain diseases; however, brain sMRI scans can be easily corrupted by artifacts, e.g., motion artifacts. To remove artifacts, deep learning (DL) algorithms have been extensively studied recently. However, their performance and the challenges [...] Read more.

Structural magnetic resonance imaging (sMRI) is progressively used to diagnose brain diseases; however, brain sMRI scans can be easily corrupted by artifacts, e.g., motion artifacts. To remove artifacts, deep learning (DL) algorithms have been extensively studied recently. However, their performance and the challenges currently faced in clinical practice (e.g., real-world robustness, hallucination and over-smoothing) have not been adequately studied in a quantitative manner. In this structured literature review, we quantitatively examined DL-based artifact correction studies (N = 30), retrieved from the major databases (i.e., Google Scholar, PubMed, Web of Science, and Scopus), which particularly focused on clinical-field-strength (defined as 1.5 Tesla (T) and above) sMRI in a non-pediatric setting. Our review suggests that current DL-based approaches exhibit promising fidelity measured by structural similarity (SSIM, 0.92 ± 0.05) index and peak signal-to-noise ratio (PSNR, 32.85 ± 4.53 dB). In addition, We identified the factors underlying hallucination or over-smoothing, which are associated with neural network (NN) architecture and the training process. This study also reveals the potential advantages, brought about by frequency-aware NN. Finally, we outline several future directions, including an emerging paradigm in DL, namely physics-informed NN (PINN). Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)

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40 pages, 1621 KB

Open AccessReview

Centralized Review of Alzheimer’s Disease and Related Dementias Biomedical Repositories and Computational Methods

by Johaan Kathilankal Jis, Kewei Chen, Chen Zhao, Lingtao Chen, Seyedamin Pouriyeh, Zongxing Xie and Yixin Xie

Bioengineering 2026, 13(6), 698; https://doi.org/10.3390/bioengineering13060698 - 18 Jun 2026

Viewed by 641

Abstract

Alzheimer’s disease and related dementias (ADRD) are neurodegenerative conditions characterized by progressive cognitive and functional decline. AD pathology is associated with extracellular amyloid-β plaques, intracellular tau neurofibrillary tangles, synaptic dysfunction, and neuronal loss. AD accounts for approximately 60–80% of dementia cases globally. In [...] Read more.

Alzheimer’s disease and related dementias (ADRD) are neurodegenerative conditions characterized by progressive cognitive and functional decline. AD pathology is associated with extracellular amyloid-β plaques, intracellular tau neurofibrillary tangles, synaptic dysfunction, and neuronal loss. AD accounts for approximately 60–80% of dementia cases globally. In 2022, AD was the seventh leading cause of death in the United States, and the number of Americans aged 65 and older living with Alzheimer’s dementia is projected to increase substantially by 2060. Despite decades of research, AD/ADRD data resources remain fragmented across clinical, imaging, genetic, genomic, and therapeutic domains. This paper addresses that gap by providing a centralized review of widely used AD/ADRD databases and computational methods. We first summarize computational approaches used to analyze these datasets, including machine learning (ML), natural language processing (NLP), and biomedical imaging. We then review eight databases classified into three categories: Clinical and Population Data, Genetics and Genomics, and Drug Discovery and Therapeutics. Finally, we discuss real-world applications, including early diagnosis, clinical decision support, personalized medicine, and drug-mechanism analysis. This review identifies opportunities for future work in data harmonization, cross-database compatibility, and robust, generalizable AI models for AD/ADRD research. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Medical Imaging Processing)

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10 pages, 3399 KB

Open AccessArticle

Practicality of Using Pressure Sensors and Accelerometers to Quantify Hand Orthosis Compliance at Home

by Devi Baruni Devanand, Matthew D. Gardiner and Angela E. Kedgley

Bioengineering 2026, 13(6), 697; https://doi.org/10.3390/bioengineering13060697 - 18 Jun 2026

Viewed by 323

Abstract

Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. [...] Read more.

Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. This study assessed the real-world practicality of using an objective compliance monitoring device with a hand orthosis. A device consisting of a pressure sensor and accelerometer was tested by ten healthy volunteers who wore a hand orthosis daily and completed a diary of their wear time and activities for a week. Sensor data obtained from the compliance monitoring device were analysed to discern each user’s orthosis wear time. Differences between estimated wear time and actual wear time were insignificant. Pressure-based wear time estimations had a specificity of 99.3 ± 0.7% and a sensitivity of 80.3 ± 19.2%, whilst acceleration-derived estimations had a specificity of 94.5 ± 6.4% and a sensitivity of 73.2 ± 15.8%. This study demonstrated that orthosis compliance can be monitored outside the laboratory, and, furthermore, this device offers insights into the intensity and frequency of a user’s activities and has the future potential to monitor orthosis fit and forces applied to affected joints using pressure. Full article

(This article belongs to the Special Issue Wearable Technologies and Health Informatics: Advancements in Bioengineering)

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17 pages, 1779 KB

Open AccessArticle

Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study

by Mariem Chouchen, Chamseddine Barki, Ismail Dergaa, Halil İbrahim Ceylan, Andrea de Giorgio, Nicola Luigi Bragazzi and Hanene Boussi Rahmouni

Bioengineering 2026, 13(6), 696; https://doi.org/10.3390/bioengineering13060696 - 18 Jun 2026

Viewed by 441

Abstract

Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk [...] Read more.

Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk assessment in occupational exposure settings. This study examined whether machine learning algorithms can serve as high-precision computational surrogates for excess relative risk estimation in NMHP. Aim: The study aimed to (i) develop and validate three machine learning algorithms for predicting the excess relative risk per unit absorbed dose for radiation-induced solid thyroid cancer (ERR/Gy.RST), (ii) characterize relationships between dosimetric and demographic features and predicted risk, and (iii) identify the optimal algorithm for deployment in occupational health surveillance. Methods: A dataset of 4657 observations was constructed from Life Span Study-derived ERR/Gy parameters, adapted to occupational low-dose conditions, using a dose-and-dose-rate effectiveness factor of 2.0, per ICRP Publication 103. Five features (gender, age at exposure, current age, distance from the I-131 source, and cumulative absorbed dose in the thyroid) were used to train a decision tree regressor (dtcr), a random forest regressor (rfr), and a multilayer perceptron (MLP) neural network algorithm. Results: Cumulative absorbed dose in the thyroid correlated positively with ERR/Gy.RST (r = 0.63, p < 0.01), while radiation source distance demonstrated a strong inverse association (r = −0.38, p < 0.01). The MLP algorithm achieved R² score = 0.999, MSE = 0.002, and MAE = 0.010, substantially outperforming the rfr (R² score = 0.700, MSE = 0.410, MAE = 0.295) and the dtcr (R² score = 0.510, MSE = 0.654, MAE = 0.289). Conclusions: The MLP algorithm provides a high-fidelity surrogate for established ERR/Gy.RST projection tools in the NMHP context, enabling computationally efficient, feature-integrated occupational radiation-induced thyroid cancer risk quantification. These findings suggest that machine learning-based surrogate modeling is a practical, scalable complement for occupational health practitioners and radiation protection officers to support individualized surveillance of radiation-induced thyroid cancer risk in nuclear medicine departments. Full article

(This article belongs to the Section Biosignal Processing)

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25 pages, 1249 KB

Open AccessArticle

Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations

by Yinbing Tian, Ziyang Wang and Li Guo

Bioengineering 2026, 13(6), 695; https://doi.org/10.3390/bioengineering13060695 - 17 Jun 2026

Viewed by 346

Abstract

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for computer-assisted diagnosis, treatment planning, and disease monitoring. However, brain tumors usually exhibit irregular, heterogeneous, and multi-scale spatial patterns with complex and ambiguous boundaries. At the same time, the performance of deep [...] Read more.

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for computer-assisted diagnosis, treatment planning, and disease monitoring. However, brain tumors usually exhibit irregular, heterogeneous, and multi-scale spatial patterns with complex and ambiguous boundaries. At the same time, the performance of deep segmentation models is often constrained by the limited availability of voxel-level annotations, which are expensive and time-consuming to obtain. To address these challenges, this paper proposes Semi-SwinUNeTR, a semi-supervised framework for 3D brain tumor segmentation with limited annotated data. The proposed method adopts SwinUNeTR as the segmentation backbone, enabling hierarchical volumetric representation learning through shifted-window self-attention while preserving the encoder–decoder structure required for dense prediction. On top of this backbone, we introduce a dual-consistency semi-supervised learning strategy, consisting of mean teacher-based model consistency and interpolation consistency-based data consistency. In addition, voxel-wise consistency weights are used to redistribute semi-supervised supervision toward structurally complex and boundary-irregular tumor regions without changing the SwinUNeTR backbone. Experiments on the BraTS 2019 benchmark demonstrate that the proposed framework achieves strong performance across different annotation ratios. The original Semi-SwinUNeTR achieves Dice scores of 84.93%, 86.25%, 87.05%, and 87.83% under the 10%, 20%, 40%, and 80% labeled-data settings, respectively. With the weighted consistency extension, the Dice scores are further improved to 85.64%, 87.94%, and 88.59% under the 10%, 20%, and 80% labeled-data settings, respectively, while the corresponding HD₉₅ values are reduced to 8.9826, 8.1854, and 7.4533. These results indicate that combining a SwinUNeTR backbone with complementary model consistency, data consistency, and voxel-wise consistency weighting is an effective strategy for semi-supervised volumetric medical image segmentation under limited annotation. Full article

(This article belongs to the Special Issue AI and Robotics for Multimodal Psychophysiological Health Monitoring)

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18 pages, 12353 KB

Open AccessArticle

Decoding Visual Pathway Dysfunction with SERF-MEG: A Study in Patients with Optic Neuropathy

by Helei Wang, Yuankun Qi, Yu Lou, Xu Zhang and Xinda Song

Bioengineering 2026, 13(6), 694; https://doi.org/10.3390/bioengineering13060694 - 17 Jun 2026

Viewed by 348

Abstract

This study aimed to characterize cortical dysfunction and frequency-specific network reorganization following optic nerve injury using spin-exchange relaxation-free magnetoencephalography (SERF-MEG), and to assess the potential of MEG-derived multiscale features as sensitive functional biomarkers for clinical evaluation. In this prospective case–control study, SERF-MEG recordings [...] Read more.

This study aimed to characterize cortical dysfunction and frequency-specific network reorganization following optic nerve injury using spin-exchange relaxation-free magnetoencephalography (SERF-MEG), and to assess the potential of MEG-derived multiscale features as sensitive functional biomarkers for clinical evaluation. In this prospective case–control study, SERF-MEG recordings were acquired during a pattern-reversal visual stimulation paradigm. Time-domain evoked components (M100/M135), global electrophysiological indices, energy-based metrics, and alpha- and beta-band phase-based functional connectivity were extracted. Network topology was quantified using graph-theoretical measures, including global and local efficiency, clustering coefficient, and assortativity. Group-level differences between patients and healthy controls were statistically analyzed. Patients showed significantly reduced M100/M135 amplitudes, prolonged M100 latency, and a lower early-component energy ratio. Functional connectivity was significantly decreased in the alpha and beta bands, accompanied by reduced global and local efficiency, mean strength, and clustering coefficient. Seed-based analyses revealed reduced connectivity predominantly in occipito-parietal and occipito-temporal pathways. SERF-MEG provides sensitive identification of cortical- and network-level functional impairments following optic nerve damage. MEG has significant clinical potential for disease diagnosis and therapy monitoring, providing a novel objective assessment tool for neuro-ophthalmological disorders. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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54 pages, 85092 KB

Open AccessReview

Advances and Prospects in MOF-Based Platforms for Tumor Hyperthermia

by Diyi Feng and Liqin Ge

Bioengineering 2026, 13(6), 693; https://doi.org/10.3390/bioengineering13060693 - 17 Jun 2026

Viewed by 469

Abstract

Metal-organic framework (MOF)-based materials have become promising platforms for tumor hyperthermia by integrating energy conversion, tumor microenvironment regulation, and multimodal therapy within programmable porous structures. This review summarizes recent advances in intrinsic MOFs, MOF composites, and MOF-derived materials for photothermal therapy, microwave hyperthermia, [...] Read more.

Metal-organic framework (MOF)-based materials have become promising platforms for tumor hyperthermia by integrating energy conversion, tumor microenvironment regulation, and multimodal therapy within programmable porous structures. This review summarizes recent advances in intrinsic MOFs, MOF composites, and MOF-derived materials for photothermal therapy, microwave hyperthermia, and magnetic hyperthermia. The reviewed studies show that high-valence metal MOFs mainly provide stable and modifiable frameworks, whereas transition-metal, magnetic, and multimetallic MOFs contribute to redox regulation, ROS generation, magnetic response, and microwave energy dissipation. Beyond localized heat generation, MOF-based platforms enhance therapeutic efficacy by combining hyperthermia with chemotherapy, chemodynamic therapy, metabolic intervention, immunotherapy, and imaging guidance. These integrated strategies help overcome incomplete ablation, thermotolerance, oxidative stress resistance, and tumor recurrence. However, clinical translation is still limited by insufficient standardization, uncertain degradation behavior, metal-ion safety, and inadequate thermal dose control. Future development should emphasize mechanism-oriented design, controllable composition, long-term biosafety, and image-guided thermal regulation to advance MOF-based hyperthermia toward precise and clinically relevant cancer therapy. Full article

(This article belongs to the Special Issue Emerging Nanomaterials and Nanotechnologies for Biomedical and Related Applications)

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31 pages, 3068 KB

Open AccessReview

Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review

by Wissem Dhahbi, Nidhal Jebabli, Marouen Souaifi, Halil İbrahim Ceylan, Helmi Ben Saad, Karim Chamari, David B. Pyne and Helmi Chaabene

Bioengineering 2026, 13(6), 692; https://doi.org/10.3390/bioengineering13060692 - 17 Jun 2026

Viewed by 493

Abstract

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary [...] Read more.

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice. Full article

(This article belongs to the Section Biosignal Processing)

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9 pages, 735 KB

Open AccessArticle

Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance

by Dominic LaBella, Michaela Kop, Xuan Qi, Hunter Stecko, Baris Turkbey, Hannah Scanlon and Thomas Sanford

Bioengineering 2026, 13(6), 691; https://doi.org/10.3390/bioengineering13060691 - 17 Jun 2026

Viewed by 297

Abstract

Background: Deep neural network based prostate segmentation depends on manual annotations, yet the effect of annotation variability on model performance remains underexplored. Methods: Prostate contours were manually delineated by an expert clinician on 119 T2-weighted MR images from the PROSTATEx Challenge 2017 training [...] Read more.

Background: Deep neural network based prostate segmentation depends on manual annotations, yet the effect of annotation variability on model performance remains underexplored. Methods: Prostate contours were manually delineated by an expert clinician on 119 T2-weighted MR images from the PROSTATEx Challenge 2017 training dataset, and slice-wise synthetic radial modifications of 1–10 mm were applied to create 10 modified training datasets plus an unmodified baseline. Identical SegResNet models were trained with Auto3DSeg/MONAI and evaluated against unmodified validation and test sets using the Dice similarity coefficient (DSC). Results: Mean test DSC decreased from 0.917 for the baseline model to 0.856 at 10 mm modification. Models trained with small annotation perturbations of 1–5 mm maintained DSC values of at least 0.90, whereas performance declined significantly beyond 5 mm. Pairwise DSC agreement across modified annotations also fell as modification amplitude increased. Conclusions: Prostate segmentation models tolerated modest annotation variability but degraded substantially when variability exceeded 5 mm, underscoring the importance of annotation quality when training and benchmarking DNN-based automated segmentation models. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging, Third Edition)

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15 pages, 1204 KB

Open AccessReview

White Esthetic Score as a Tool for Esthetic Assessment of Tooth-Supported Restorations: A Comprehensive Review with Case Illustration

by Abdulrahman Alshabib, Silvia Rojas-Rueda, Jose Villalobos-Tinoco, Khalid M. Aldosary, Francisco Garcia-Torres, Carlos A. Jurado and Mark A. Antal

Bioengineering 2026, 13(6), 690; https://doi.org/10.3390/bioengineering13060690 - 16 Jun 2026

Viewed by 363

Abstract

Background: The White Esthetic Score (WES) is a standardized clinician-reported index that assesses the esthetic quality of a single-tooth restoration by comparison with a natural reference tooth, typically the contralateral tooth. It evaluates five domains: tooth form, crown outline/volume, color (hue/value), surface texture, [...] Read more.

Background: The White Esthetic Score (WES) is a standardized clinician-reported index that assesses the esthetic quality of a single-tooth restoration by comparison with a natural reference tooth, typically the contralateral tooth. It evaluates five domains: tooth form, crown outline/volume, color (hue/value), surface texture, and translucency/characterization. Each domain is scored from 0 to 2 (major discrepancy, minor discrepancy, no discrepancy), yielding a total score of 0–10; higher scores indicate a closer match. Although developed for single-tooth implant restorations, WES has also been applied to natural teeth and tooth-supported restorations. Methods: This comprehensive review summarizes case-report evidence applying WES to tooth-supported restorations, outlining the concept, scoring method, documentation requirements, and available data on reliability and interpretation. A case illustration is also presented in which a patient received eight anterior veneers; outcomes were assessed using all WES parameters. Results: Published reports support WES as a practical qualitative tool to assess esthetic outcomes in tooth-supported restorations. In the presented case, the veneers achieved a WES of 9, reflecting marked improvement in tooth form, crown outline/volume, color, surface texture, and translucency/characterization. Conclusions: The comprehensive review indicates WES is feasible for routine clinical use in practice, but agreement varies by parameter and improves with standardized photography and examiner calibration; some components show lower inter-rater agreement than simpler soft-tissue indices. Because correlations between WES and patient satisfaction are inconsistent, WES should be complemented with patient-reported outcome measures. Common thresholds consider WES ≥ 6 acceptable. Clinical use for crowns and veneers should emphasize case selection, standardized records, and combined clinician- and patient-centered outcome reporting. Full article

(This article belongs to the Special Issue New Tools for Multidisciplinary Treatment in Dentistry, 2nd Edition)

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16 pages, 3722 KB

Open AccessArticle

Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers

by Carolina Duque-Mejia, Camilo Zapata-Hernandez, Eduardo Duque-Grisales, Leonardo Serna-Guarin, Gustavo Lodoño-Ossa and Miguel A. Becerra

Bioengineering 2026, 13(6), 689; https://doi.org/10.3390/bioengineering13060689 - 16 Jun 2026

Viewed by 349

Abstract

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research [...] Read more.

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions. Full article

(This article belongs to the Special Issue Generative AI for Biosignal and Medical Imaging Analysis)

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14 pages, 340 KB

Open AccessReview

Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications

by Lucia Maresca, Antonio D’Amato, Camilla Coianiz, Alessandra Cavalieri, Erica Vella, Francesco Gebbia, Lorena Bori and Marcos Meseguer

Bioengineering 2026, 13(6), 688; https://doi.org/10.3390/bioengineering13060688 - 16 Jun 2026

Viewed by 473

Abstract

Embryo selection remains one of the main unresolved challenges in in vitro fertilization, despite major advances in assisted reproductive technologies. Conventional assessment is still largely based on morphological evaluation, which is limited by subjectivity, static observation, and the difficulty of integrating heterogeneous clinical [...] Read more.

Embryo selection remains one of the main unresolved challenges in in vitro fertilization, despite major advances in assisted reproductive technologies. Conventional assessment is still largely based on morphological evaluation, which is limited by subjectivity, static observation, and the difficulty of integrating heterogeneous clinical and biological data. In recent years, artificial intelligence has emerged as a decision-support tool in embryology, enabling the analysis of large datasets derived from embryo images, morphokinetic parameters, and clinical variables. This review summarizes current approaches to artificial intelligence in embryo selection, including models based on static images and time-lapse imaging data. Machine learning and deep learning techniques are discussed, including convolutional neural networks and spatiotemporal models. The evaluation of model performance is also examined, highlighting the clinical relevance of endpoints such as time to live birth compared with traditional outcome measures. Finally, ethical and clinical implications are considered, including issues related to transparency, responsibility, human oversight, and regulation. Artificial intelligence has the potential to improve embryo selection, although further validation and standardized implementation are needed before routine clinical use. Full article

(This article belongs to the Special Issue Artificial Intelligence in Reproductive Medicine: Challenges and New Perspectives)

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19 pages, 1688 KB

Open AccessArticle

Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs

by Sercan Taskin, Serra Aksoy, Mine Gecgelen Cesur, Pinar Demircioglu and Ismail Bogrekci

Bioengineering 2026, 13(6), 687; https://doi.org/10.3390/bioengineering13060687 - 15 Jun 2026

Viewed by 368

Abstract

Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline [...] Read more.

Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline conditions. Materials and Methods: A total of 146 standardized frontal photographs (72 with midline deviation ≥ 2 mm from the facial midline, defined by the soft-tissue nasion–subnasal line; 74 symmetric) were analyzed. YOLOv8 was used to obtain bounding-box and keypoint predictions, which were converted into a numerical feature vector and used to train 11 classifiers (including Naive Bayes, Logistic Regression with L1 and ElasticNet penalties, Support Vector Machine, AdaBoost, and others). Performance was assessed using accuracy (with 95% Wilson confidence intervals), precision, recall, F1-score, and ROC-AUC. Optimization of hyperparameters for the downstream classifiers employed five-fold cross-validation along with grid search inside the training data set (n = 126) while final classifier assessment was done using a reserved test data set (n = 20). As the YOLOv8 object detector was trained using the full image dataset before extracting features, the classification metrics presented here should be considered as exploratory results only. Results: YOLOv8 achieved mAP@0.5 = 0.995 for midline detection. Naive Bayes attained the highest classification accuracy of 75% (95% CI: 53–89%) with ROC-AUC = 0.75. AdaBoost achieved 65% (95% CI: 43–82%). Several models defaulted to majority-class prediction (accuracy = 40%), indicating insufficient feature discriminability. Conclusions: YOLOv8 detected the maxillary dental midline under the present internal experimental conditions. However, because leakage-free outer k-fold validation of the complete detection-plus-classification pipeline was not performed, the classification results should be considered preliminary. Future work should address information leakage, incorporate facial reference frame normalization, include inter-observer reliability assessment, and validate the approach on larger datasets. Full article

(This article belongs to the Special Issue Machine Learning and Computer Vision for Dental and Oral Surgical Applications)

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25 pages, 715 KB

Open AccessArticle

An Agentic LLM Framework for Autonomous Surgical Continuum Monitoring: ReAct-Driven Tool-Use Agents for Presurgical, Intraoperative, and Postsurgical Cardiopulmonary Care

by Charalampia Pylarinou, Lefteris Gortzis, Vasileios Leivaditis, Elias Liolis, Andreas Antzoulas, Spyros Papadoulas, Konstantinos Nikolakopoulos, Ioannis Panagiotopoulos, Sofoklis Mitsos, Periklis Tomos, Efstratios Koletsis and Francesk Mulita

Bioengineering 2026, 13(6), 686; https://doi.org/10.3390/bioengineering13060686 - 15 Jun 2026

Viewed by 460

Abstract

Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical continuum care, where patients traverse presurgical risk stratification, intraoperative monitoring, postsurgical ICU, [...] Read more.

Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical continuum care, where patients traverse presurgical risk stratification, intraoperative monitoring, postsurgical ICU, ward care, and remote rehabilitation over days to weeks—a complexity no fixed-policy agent architecture can address without prohibitive rule engineering. Objective: We present the first agentic large language model (LLM) framework for autonomous end-to-end surgical continuum monitoring, superseding the prior rule-based MAS Digital Twin. Six ReAct-driven tool-use agents replace fixed-policy agents with dynamic reasoning, multi-hop evidence retrieval, and Reflexion self-correction while maintaining mandatory confidence-gated Human-in-the-Loop (HITL) gating at every care-pathway-modifying decision. Methods: The framework is grounded in the ReAct paradigm and Reflexion self-evaluation, embedded within the DETER Digital Twin state engine S(t). Each agent is specified by a ReAct loop signature, a ten-function clinical tool registry, and confidence-gated HITL escalation logic. Inter-agent coordination replaces the rule-based Priority Queue Manager with an LLM-mediated Coordination Supervisor Agent reasoning over competing resource requests. Results: The framework delivers: (i) six formally specified ReAct-loop agents with explicit tool registries and authorisation boundaries; (ii) a confidence-gated HITL architecture that reduces alert fatigue while preserving safety for ambiguous clinical scenarios; (iii) an extended conflict resolution function P(p,t,context) incorporating surgical phase and DETER deterioration trajectory gradient; (iv) Reflexion self-correction with a formal N_max = 2 termination condition and Clinical Factuality Verification Layer; and (v) a multi-phase Digital Twin state engine extending S(t) to the full surgical continuum. Conclusions: The proposed framework represents a fundamental architectural departure from rule-based clinical AI—from hardcoded policies to dynamic reasoning, from static retrieval to multi-hop tool-use chains, and from fixed escalation thresholds to confidence-gated self-evaluation—providing a formally specified, clinically deployable foundation for next-generation autonomous surgical care coordination. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Bioengineering: Second Edition)

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18 pages, 3643 KB

Open AccessArticle

Clinical and Wavefront Outcomes After Femtosecond Laser Versus Mechanical Microkeratome Lasik: A Prospective Paired-Eye Comparative Study

by Sophie-Charlotte Drogge, Andreas J. Kreis, Ivo Guber, Valentin Pajic, Vladimir Canadanovic, Zeljka Cvejic, Martina Kropp, Gabriele Thumann, Eline De Clerck, Mirko Resan, Bogdan Resan and Bojan Pajic

Bioengineering 2026, 13(6), 685; https://doi.org/10.3390/bioengineering13060685 - 14 Jun 2026

Viewed by 313

Abstract

Background/Objectives: The technique used for flap creation in laser in situ keratomileusis (LASIK) may influence postoperative optical quality and visual outcomes. This prospective randomized paired-eye study compared higher-order aberrations (HOAs) and visual acuity outcomes following femtosecond laser-assisted versus mechanical microkeratome-assisted LASIK. Materials [...] Read more.

Background/Objectives: The technique used for flap creation in laser in situ keratomileusis (LASIK) may influence postoperative optical quality and visual outcomes. This prospective randomized paired-eye study compared higher-order aberrations (HOAs) and visual acuity outcomes following femtosecond laser-assisted versus mechanical microkeratome-assisted LASIK. Materials and Methods: Forty-four patients (88 eyes) underwent bilateral LASIK. In each patient, one eye was randomly assigned to high-frequency femtosecond laser flap creation (Femto LDV), and the fellow eye to mechanical microkeratome flap creation (Amadeus II). Inclusion criteria were stable refraction, central corneal thickness ≥ 520 µm, and normal corneal topography. HOAs were measured using Hartmann–Shack wavefront aberrometry over a 6 mm pupil diameter. Uncorrected and corrected distance visual acuity (UDVA and CDVA) were evaluated preoperatively and postoperatively at 1 day, 1 week, and 1, 3, and 6 months. Results: Both techniques induced significant postoperative changes in specific Zernike coefficients and an increase in total HOA root mean square (RMS) values (p < 0.05). A reduction in spherical aberration (Z4,0) was observed in both groups, while technique-specific changes were noted in individual aberration components including an increase in horizontal trefoil (Z3,3) in the femtosecond and a decrease in horizontal coma (Z5,1) in the microkeratome group. However, paired-eye comparisons revealed no statistically significant differences in total HOA six months postoperative. Despite comparable aberrometric outcomes, femtosecond-treated eyes demonstrated significantly better UDVA and CDVA at all postoperative time points (p < 0.05). Conclusions: Femtosecond laser-assisted and microkeratome-assisted LASIK resulted in comparable changes in higher-order aberrations, despite differing pattern in individual aberration components. The observed differences in visual acuity outcomes were not reflected in wavefront metrics, suggesting that postoperative visual performance may be influenced by factors. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

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14 pages, 859 KB

Open AccessArticle

Development of a Conceptual Implant Stability Index Framework for Computational Risk Assessment in Implant Dentistry

by Liliana Sachelarie, Corina Laura Ștefănescu, Rodica Maria Murineanu, Mircea Grigorian, Agripina Zaharia and Loredana Liliana Hurjui

Bioengineering 2026, 13(6), 684; https://doi.org/10.3390/bioengineering13060684 - 12 Jun 2026

Viewed by 463

Abstract

(1) Background: Dental implant stability is influenced by multiple biomechanical, implant-related, and systemic factors, including bone density, implant geometry, biomechanical loading, smoking, osteoporosis, and diabetes mellitus. Computational bioengineering approaches may facilitate theoretical assessment of implant stability and support future risk-evaluation strategies. The [...] Read more.

(1) Background: Dental implant stability is influenced by multiple biomechanical, implant-related, and systemic factors, including bone density, implant geometry, biomechanical loading, smoking, osteoporosis, and diabetes mellitus. Computational bioengineering approaches may facilitate theoretical assessment of implant stability and support future risk-evaluation strategies. The aim of this study was to develop a conceptual computational framework for assessing theoretical implant instability using clinically relevant biomechanical and systemic parameters. (2) Methods: A multivariable computational framework was developed by integrating bone density, implant dimensions, implant mobility indicators, biomechanical loading conditions, smoking status, osteoporosis, and diabetes mellitus into a conceptual Implant Stability Index (ISI). Computational simulations and theoretical risk stratification procedures were used to evaluate framework behavior under different simulated conditions. (3) Results: The framework demonstrated the theoretical ability to differentiate between favorable and unfavorable implant stability conditions. Reduced bone density, increased implant mobility indicators, excessive biomechanical loading, and adverse systemic factors resulted in lower calculated ISI values and a higher theoretical instability risk. The framework further enabled the classification of simulated conditions into high-, moderate-, and increased-instability-risk categories. (4) Conclusions: The proposed Implant Stability Index represents a conceptual computational framework for integrating biomechanical, implant-related, and systemic factors associated with implant stability. Although not clinically validated, the framework may provide a proof-of-concept foundation for future studies involving clinical datasets, biomechanical simulations, and advanced computational modeling approaches. Full article

(This article belongs to the Special Issue Dental Implant Reconstruction and Biomechanical Evaluation, 3rd Edition)

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Bioengineering, Volume 13, Issue 6 (June 2026) – 122 articles

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