Bioengineering

22 pages, 3608 KiB

Open AccessArticle

AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania’s First Wave of COVID-19

by Sonja C. S. Simon, Igor Bibi, Daniel Schaffert, Johannes Benecke, Niklas Martin, Jan Leipe, Cristian Vladescu and Victor Olsavszky

Bioengineering 2024, 11(12), 1272; https://doi.org/10.3390/bioengineering11121272 (registering DOI) - 15 Dec 2024

Abstract

Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises. [...] Read more.

Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises. Methods: AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January–September 2020). Results: For discharge outcomes, Light Gradient Boosted models achieved an F1 score of 0.9644, while for mortality 0.7545 was reached. A Generalized Linear Model blender achieved an F1 score of 0.9884 for “acute or emergency” cases, and an average blender reached 0.923 for COVID-19 cases. Older age, specific hospitals, and oncology wards were less associated with improved recovery rates, while mortality was linked to abnormal lab results and cardiovascular/respiratory diseases. Patients admitted without referral, or patients in hospitals in the central region and the capital region of Romania were more likely to be acute cases. Finally, counties such as Argeş (South-Muntenia) and Brașov (Center) showed higher COVID-19 infection rates regardless of age. Conclusions: AutoML provided valuable insights into patient outcomes, highlighting variations in care and the need for targeted health strategies for both COVID-19 and other health challenges. Full article

(This article belongs to the Special Issue Artificial Intelligence in Healthcare)

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16 pages, 4411 KiB

Open AccessArticle

Evaluation of a Semi-Automated Ultrasound Guidance System for Central Vascular Access

by Sofia I. Hernandez Torres, Nicole W. Caldwell and Eric J. Snider

Bioengineering 2024, 11(12), 1271; https://doi.org/10.3390/bioengineering11121271 (registering DOI) - 15 Dec 2024

Abstract

Hemorrhage remains a leading cause of death in both military and civilian trauma settings. Oftentimes, the control and treatment of hemorrhage requires central vascular access and well-trained medical personnel. Automated technology is being developed that can lower the skill threshold for life-saving interventions. [...] Read more.

Hemorrhage remains a leading cause of death in both military and civilian trauma settings. Oftentimes, the control and treatment of hemorrhage requires central vascular access and well-trained medical personnel. Automated technology is being developed that can lower the skill threshold for life-saving interventions. Here, we conduct independent evaluation testing of one such device, the Vu-Path™ Ultrasound Guidance system, or Vu-Path™. The device was designed to simplify needle insertion using a needle holder that ensures the needle is within the ultrasound field of view during its insertion into tissue, along with guidance lines shown on the user interface. We evaluated the performance of this device in a range of laboratory, animal, and human testing platforms. Overall, the device had a high success rate, achieving an 83% insertion accuracy in live animal testing across both normal and hypotensive blood pressures. Vu-Path™ was faster than manual, ultrasound-guided needle insertion and was nearly 1.5 times quicker for arterial and 2.3 times quicker for venous access. Human usability feedback highlighted that 80% of the participants would use this device for central line placement. Study users noted that the guidance lines and small form factor were useful design features. However, issues were raised regarding the needle insertion angle being too steep, with potential positioning challenges as the needle remains fixed to the ultrasound probe. Regardless, 75% of the participants believed that personnel with any level of clinical background could use the device for central vascular access. Overall, Vu-Path™ performed well across a range of testing situations, and potential design improvements were noted. With adjustments to the device, central vascular access can be made more accessible on battlefields in the future. Full article

(This article belongs to the Special Issue Devices for Vascular Intervention)

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20 pages, 15019 KiB

Open AccessArticle

Long-Term Histological Evaluation of a Novel Dermal Template in the Treatment of Pediatric Burns

by Zeena Gerster-Barzanji, Vivienne Woodtli, Mira Klix, Thomas Biedermann, Clemens Schiestl, Kathrin Neuhaus, Melinda Farkas, Jivko Kamarachev, Daniel Rittirsch and Sophie Böttcher-Haberzeth

Bioengineering 2024, 11(12), 1270; https://doi.org/10.3390/bioengineering11121270 (registering DOI) - 14 Dec 2024

Abstract

For pediatric patients with full-thickness burns, achieving adequate dermal regeneration is essential to prevent inelastic scars that may hinder growth. Traditional autologous split-thickness skin grafts alone often fail to restore the dermal layer adequately. This study evaluates the long-term effect of using a [...] Read more.

For pediatric patients with full-thickness burns, achieving adequate dermal regeneration is essential to prevent inelastic scars that may hinder growth. Traditional autologous split-thickness skin grafts alone often fail to restore the dermal layer adequately. This study evaluates the long-term effect of using a NovoSorb^® Biodegradable Temporizing Matrix (BTM) as a dermal scaffold in four pediatric patients, promoting dermal formation before autografting. Pediatric burn patients treated at the University Children’s Hospital Zurich between 2020 and 2022 underwent a two-step treatment involving NovoSorb^® BTM application, followed by autografting. Histological analysis, conducted through 22 punch biopsies taken up to 2.6 years post-application, demonstrated robust dermal reorganization, with mature epidermal regeneration and stable dermo-epidermal connections. Immunofluorescence staining showed rapid capillary ingrowth, while extracellular matrix components, including collagen and elastic fibers, gradually aligned over time, mimicking normal skin structure. By 2.6 years, the dermal layer displayed characteristics close to uninjured skin, with remnants of NovoSorb^® BTM degrading within five months post-application. This study suggests that NovoSorb^® BTM facilitates elastic scar formation, offering significant benefits for pediatric patients by reducing functional limitations associated with inelastic scarring. Full article

(This article belongs to the Special Issue Insights in Tissue Engineering: Novel Developments, Current Challenges, and Future Perspectives)

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17 pages, 1405 KiB

Open AccessArticle

Temporal Tissue Remodeling in Volumetric Muscle Injury with Endothelial Cell-Laden Patterned Nanofibrillar Constructs

by Krista M. Habing, Cynthia A. Alcazar, Nathaniel Dobson, Yong How Tan, Ngan F. Huang and Karina H. Nakayama

Bioengineering 2024, 11(12), 1269; https://doi.org/10.3390/bioengineering11121269 (registering DOI) - 14 Dec 2024

Abstract

A primary challenge following severe musculoskeletal trauma is incomplete muscle regeneration. Current therapies often fail to heal damaged muscle due to dysregulated healing programs and insufficient revascularization early in the repair process. There is a limited understanding of the temporal changes that occur [...] Read more.

A primary challenge following severe musculoskeletal trauma is incomplete muscle regeneration. Current therapies often fail to heal damaged muscle due to dysregulated healing programs and insufficient revascularization early in the repair process. There is a limited understanding of the temporal changes that occur during the early stages of muscle remodeling in response to engineered therapies. Previous work demonstrated that nanotopographically patterned scaffolds provide cytoskeletal guidance and direct endothelial angiogenic and anti-inflammatory phenotypes. The aim of this study was to evaluate how endothelial cell (EC) patterning guides temporal and histomorphological muscle remodeling after muscle injury. In the current study, mice were treated with EC-laden engineered constructs that exhibited either aligned or random patterning of collagen nanofibrils, following a volumetric muscle loss injury (VML). Remodeling was evaluated at 2, 7, and 21 days post injury. Over the 21-day study, all groups (Acellular Aligned, EC Aligned, EC Random) demonstrated similar significant increases in vascular density and myogenesis. Animals treated with acellular controls demonstrated a two-fold decrease in muscle cross-sectional area between days 2 and 21 post injury, consistent with VML-induced muscle atrophy; however, animals treated with patterned EC-laden constructs exhibited preservation of muscle mass. The implantation of an EC-laden construct led to a 50% increase in the number of animals exhibiting areas of fibrous remodeling adjacent to the construct, along with greater collagen deposition (p < 0.01) compared to acellular controls 21 days post injury. These findings suggest that nanotopographically patterned EC-laden constructs may guide early muscle-protective programs that support muscle mass retention through myo-vascular independent pathways. Full article

(This article belongs to the Special Issue Advancements in Tissue-Engineered Muscle)

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14 pages, 5653 KiB

Open AccessArticle

Uterine Repair Mechanisms Are Potentiated by Mesenchymal Stem Cells and Decellularized Tissue Grafts Through Elevated Vegf, Cd44, and Itgb1 Gene Expression

by Sara Bandstein, Lucia De Miguel-Gómez, Edina Sehic, Emy Thorén, Sara López-Martínez, Irene Cervelló, Randa Akouri, Mihai Oltean, Mats Brännström and Mats Hellström

Bioengineering 2024, 11(12), 1268; https://doi.org/10.3390/bioengineering11121268 (registering DOI) - 14 Dec 2024

Abstract

Transplantation of decellularized uterus tissue showed promise in supporting regeneration following uterine injury in animal models, suggesting an alternative to complete uterus transplantation for uterine factor infertility treatment. However, most animal studies utilized small grafts, limiting their clinical relevance. Hence, we used larger [...] Read more.

Transplantation of decellularized uterus tissue showed promise in supporting regeneration following uterine injury in animal models, suggesting an alternative to complete uterus transplantation for uterine factor infertility treatment. However, most animal studies utilized small grafts, limiting their clinical relevance. Hence, we used larger grafts (20 × 10 mm), equivalent to nearly one uterine horn in rats, to better evaluate the bioengineering challenges associated with structural support, revascularization, and tissue regeneration. We analyzed histopathology, employed immunohistochemistry, and investigated gene expression discrepancies in growth-related proteins over four months post-transplantation in acellular grafts and those recellularized (RC) with bone marrow-derived mesenchymal stem cells (bmMSCs). RC grafts exhibited less inflammation and faster epithelialization and migration of endogenous cells into the graft compared with acellular grafts. Despite the lack of a significant difference in the density of CD31 positive blood vessels between groups, the RC group demonstrated a better organized myometrial layer and an overall faster regenerative progress. Elevated gene expression for Vegf, Cd44, and Itgb1 correlated with the enhanced tissue regeneration in this group. Elevated Tgfb expression was noted in both groups, potentially contributing to the rapid revascularization. Our findings suggest that large uterine injuries can be regenerated using decellularized tissue, with bmMSCs enhancing the endogenous repair mechanisms. Full article

(This article belongs to the Section Regenerative Engineering)

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24 pages, 3648 KiB

Open AccessReview

Artificial Intelligence in Dentistry: A Descriptive Review

by Sreekanth Kumar Mallineni, Mallika Sethi, Dedeepya Punugoti, Sunil Babu Kotha, Zikra Alkhayal, Sarah Mubaraki, Fatmah Nasser Almotawah, Sree Lalita Kotha, Rishitha Sajja, Venkatesh Nettam, Amar Ashok Thakare and Srinivasulu Sakhamuri

Bioengineering 2024, 11(12), 1267; https://doi.org/10.3390/bioengineering11121267 - 13 Dec 2024

Abstract

Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the [...] Read more.

Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the globe in the healthcare industry. Advancements in machine learning and data analysis have revolutionized oral health diagnosis, treatment, and management, making it a transformative force in healthcare, particularly in dentistry. Particularly in dentistry, AI is becoming increasingly prevalent as it contributes to the diagnosis of oro-facial diseases, offers treatment modalities, and manages practice in the dental operatory. All dental disciplines, including oral medicine, operative dentistry, pediatric dentistry, periodontology, orthodontics, oral and maxillofacial surgery, prosthodontics, and forensic odontology, have adopted AI. The majority of AI applications in dentistry are for diagnoses based on radiographic or optical images, while other tasks are less applicable due to constraints such as data availability, uniformity, and computational power. Evidence-based dentistry is considered the gold standard for decision making by dental professionals, while AI machine learning models learn from human expertise. Dentistry AI and technology systems can provide numerous benefits, such as improved diagnosis accuracy and increased administrative task efficiency. Dental practices are already implementing various AI applications, such as imaging and diagnosis, treatment planning, robotics and automation, augmented and virtual reality, data analysis and predictive analytics, and administrative support. The dentistry field has extensively used artificial intelligence to assist less-skilled practitioners in reaching a more precise diagnosis. These AI models effectively recognize and classify patients with various oro-facial problems into different risk categories, both individually and on a group basis. The objective of this descriptive review is to review the most recent developments of AI in the field of dentistry. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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12 pages, 1677 KiB

Open AccessReview

Microfluidic Technology for Measuring Mechanical Properties of Single Cells and Its Application

by Yixin Yin and Ziyuan Liu

Bioengineering 2024, 11(12), 1266; https://doi.org/10.3390/bioengineering11121266 - 13 Dec 2024

Abstract

Cellular mechanical properties are critical for tissue and organ homeostasis, which are associated with many diseases and are very promising non-labeled biomarkers. Over the past two decades, many research tools based on microfluidic methods have been developed to measure the biophysical properties of [...] Read more.

Cellular mechanical properties are critical for tissue and organ homeostasis, which are associated with many diseases and are very promising non-labeled biomarkers. Over the past two decades, many research tools based on microfluidic methods have been developed to measure the biophysical properties of single cells; however, it has still not been possible to develop a technique that allows for high-throughput, easy-to-operate and precise measurements of single-cell biophysical properties. In this paper, we review the emerging technologies implemented based on microfluidic approaches for characterizing the mechanical properties of single cells and discuss the methodological principles, advantages, limitations, and applications of various technologies. Full article

(This article belongs to the Section Biomechanics and Sports Medicine)

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25 pages, 5211 KiB

Open AccessArticle

A Novel Grammar-Based Approach for Patients’ Symptom and Disease Diagnosis Information Dissemination to Maintain Confidentiality and Information Integrity

by Sanjay Nag, Nabanita Basu, Payal Bose and Samir Kumar Bandyopadhyay

Bioengineering 2024, 11(12), 1265; https://doi.org/10.3390/bioengineering11121265 - 13 Dec 2024

Abstract

Disease prediction using computer-based methods is now an established area of research. The importance of technological intervention is necessary for the better management of disease, as well as to optimize use of limited resources. Various AI-based methods for disease prediction have been documented [...] Read more.

Disease prediction using computer-based methods is now an established area of research. The importance of technological intervention is necessary for the better management of disease, as well as to optimize use of limited resources. Various AI-based methods for disease prediction have been documented in the literature. Validated AI-based systems support diagnoses and decision making by doctors/medical practitioners. The resource-efficient dissemination of the symptoms identified and the diagnoses undertaken is the requirement of the present-day scenario to support paperless, yet seamless, information sharing. The representation of symptoms using grammar provides a novel way for the resource-efficient encoding of disease diagnoses. Initially, symptoms are represented as strings, and, in terms of grammar, this is called a sentence. Moreover, the conversion of the generated string containing the symptoms and the diagnostic outcome to a QR code post encryption makes it portable. The code can be stored in a mobile application, in a secure manner, and can be scanned wherever required, universally. The patient can carry the medical condition and the diagnosis in the form of the QR code for medical consultations. This research work presents a case study based on two diseases, influenza and coronavirus, to highlight the proposed methodology. Both diseases have some common and overlapping symptoms. The proposed system can be implemented for any kind of disease detection, including clinical and diagnostic imaging. Full article

(This article belongs to the Section Biosignal Processing)

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11 pages, 1083 KiB

Open AccessArticle

The Classification of Metastatic Spine Cancer and Spinal Compression Fractures by Using CNN and SVM Techniques

by Woosik Jeong, Chang-Heon Baek, Dong-Yeong Lee, Sang-Youn Song, Jae-Boem Na, Mohamad Soleh Hidayat, Geonwoo Kim and Dong-Hee Kim

Bioengineering 2024, 11(12), 1264; https://doi.org/10.3390/bioengineering11121264 - 13 Dec 2024

Abstract

Metastatic spine cancer can cause pain and neurological issues, making it challenging to distinguish from spinal compression fractures using magnetic resonance imaging (MRI). To improve diagnostic accuracy, this study developed artificial intelligence (AI) models to differentiate between metastatic spine cancer and spinal compression [...] Read more.

Metastatic spine cancer can cause pain and neurological issues, making it challenging to distinguish from spinal compression fractures using magnetic resonance imaging (MRI). To improve diagnostic accuracy, this study developed artificial intelligence (AI) models to differentiate between metastatic spine cancer and spinal compression fractures in MRI images. MRI data from Gyeongsang National University Hospital, collected from January 2019 to April 2022, were processed using Otsu’s binarization and Canny edge detection algorithms. Using these preprocessed datasets, convolutional neural network (CNN) and support vector machine (SVM) models were built. The T1-weighted image-based CNN model demonstrated high sensitivity (1.00) and accuracy (0.98) in identifying metastatic spine cancer, particularly with data processed by Otsu’s binarization and Canny edge detection, achieving exceptional performance in detecting cancerous cases. This approach highlights the potential of preprocessed MRI data for AI-assisted diagnosis, supporting clinical applications in distinguishing metastatic spine cancer from spinal compression fractures. Full article

(This article belongs to the Section Biosignal Processing)

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10 pages, 1250 KiB

Open AccessArticle

An Observational Study on the Prediction of Range of Motion in Soldiers Diagnosed with Patellar Tendinopathy Using Ultrasound Shear Wave Elastography

by Min-Woo Kim, Dong-Ha Lee and Young-Chae Seo

Bioengineering 2024, 11(12), 1263; https://doi.org/10.3390/bioengineering11121263 - 13 Dec 2024

Abstract

Introduction: This study hypothesized that changes in the elasticity of the quadriceps and patellar tendons before and after the diagnosis of patellar tendinopathy would correlate with the range of motion (ROM) following conservative treatment. We aimed to prospectively assess post-treatment ROM using multinomial [...] Read more.

Introduction: This study hypothesized that changes in the elasticity of the quadriceps and patellar tendons before and after the diagnosis of patellar tendinopathy would correlate with the range of motion (ROM) following conservative treatment. We aimed to prospectively assess post-treatment ROM using multinomial logistic regression, incorporating elasticity measurements obtained via shear wave elastography (SWE). Materials and Methods: From March 2023 to April 2024, 95 patients (86 men; aged 20–45 years, mean 25.62 ± 5.49 years) underwent SWE preoperatively and two days post-diagnosis of patellar tendinopathy. Elasticity measurements of the rectus femoris, vastus medialis, vastus lateralis, patellar tendon, and biceps tendon were obtained during full flexion and extension. Based on ROM 56 days post-treatment, patients were categorized into two groups: Group A (ROM > 120 degrees) and Group B (ROM < 120 degrees). A multinomial logistic regression algorithm was employed to classify the groups using patient information and tendon elasticity measurements both at diagnosis and 1-week post-diagnosis. Results: The predictive accuracy using only patient information was 62%, while using only elasticity measurements yielded 68% accuracy. When combining patient information with elasticity measurements taken at diagnosis and two days post-diagnosis, the algorithm achieved an accuracy of 79%, sensitivity of 92%, and specificity of 56%. Conclusions: The combination of patient information and tendon elasticity measurements obtained via SWE at pre-conservative treatment and early post-conservative treatment periods effectively predicts post-treatment ROM. This algorithm can guide rehabilitation strategies for soldiers with patellar tendinopathy. Full article

(This article belongs to the Special Issue New Sights of Deep Learning and Biomedical Image Processing: Updates, Applications, and Directions)

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27 pages, 4692 KiB

Open AccessArticle

Predicting the Impact of Polysulfone Dialyzers and Binder Dialysate Flow Rate on Bilirubin Removal

by Alexander Novokhodko, Nanye Du, Shaohang Hao, Ziyuan Wang, Zhiquan Shu, Suhail Ahmad and Dayong Gao

Bioengineering 2024, 11(12), 1262; https://doi.org/10.3390/bioengineering11121262 - 12 Dec 2024

Abstract

Liver failure is the 12th leading cause of death worldwide. Protein-bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin or another binding molecule in dialysate and detoxifying sorbent columns to remove these toxins. Systems [...] Read more.

Liver failure is the 12th leading cause of death worldwide. Protein-bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin or another binding molecule in dialysate and detoxifying sorbent columns to remove these toxins. Systems like the molecular adsorbent recirculating system and BioLogic-DT have existed since the 1990s, but survival benefits in randomized controlled trials have not been consistent. New binder dialysis systems, including open albumin dialysis and the Advanced Multi-Organ Replacement system, are being developed. Optimal conditions for binder dialysis have not been established. We developed and validated a computational model of bound solute dialysis. It predicted the impact of changing between two test setups using different polysulfone dialyzers (F3 and F6HPS). We then predicted the impact of varying the dialysate flow rate on toxin removal. We found that bilirubin removal declines with dialysate flow rate. This can be explained through a linear decline in free bilirubin membrane permeability. Our model quantifies this decline through a single parameter (polysulfone dialyzers). Validation for additional dialyzers and flow rates will be needed. This model will benefit clinical trials by predicting optimal dialyzer and flow rate conditions. Accounting for toxin adsorption onto the dialyzer membrane may improve results further. Full article

(This article belongs to the Special Issue The Power of Models and Simulation Tools in Biomedical and Biochemical Engineering)

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130 pages, 72266 KiB

Open AccessArticle

Gender Differences in the Dynamics and Kinematics of Running and Their Dependence on Footwear

by Tizian Scharl, Michael Frisch and Franz Konstantin Fuss

Bioengineering 2024, 11(12), 1261; https://doi.org/10.3390/bioengineering11121261 - 12 Dec 2024

Abstract

Previous studies on gender differences in running biomechanics have predominantly been limited to joint angles and have not investigated a potential influence of footwear condition. This study shall contribute to closing this gap. Lower body biomechanics of 37 recreational runners (19 f, 18 [...] Read more.

Previous studies on gender differences in running biomechanics have predominantly been limited to joint angles and have not investigated a potential influence of footwear condition. This study shall contribute to closing this gap. Lower body biomechanics of 37 recreational runners (19 f, 18 m) were analysed for eight footwear and two running speed conditions. Presenting the effect size Cliff’s Delta enabled the interpretation of gender differences across a variety of variables and conditions. Known gender differences such as a larger range of hip movement in female runners were confirmed. Further previously undiscovered gender differences in running biomechanics were identified. In women, the knee extensors are less involved in joint work. Instead, compared to men, the supinators contribute more to deceleration and the hip abductors to acceleration. In addition to differences in extent, women also show a temporal delay within certain variables. For the foot, ankle and shank, as well as for the distribution of joint work, gender differences were found to be dependent on footwear condition, while sagittal pelvis and non-sagittal hip and thigh kinematics are rather consistent. On average, smaller gender differences were found for an individual compared to a uniform running speed. Future studies on gender differences should consider the influence of footwear and running speed and should provide an accurate description of the footwear condition used. The findings of this study could be used for the development of gender-specific running shoes and sports and medical products and provide a foundation for the application of smart wearable devices in gender-specific training and rehabilitation. Full article

(This article belongs to the Section Biomechanics and Sports Medicine)

3 pages, 149 KiB

Open AccessEditorial

Unveiling Complexity: Mathematical Models in Aortic Disease Investigation

by Hwa Liang Leo and Hamed Keramati

Bioengineering 2024, 11(12), 1260; https://doi.org/10.3390/bioengineering11121260 (registering DOI) - 12 Dec 2024

Abstract

The complexity of aortic diseases demands sophisticated modeling approaches to better understand their pathophysiology and optimize treatment strategies [...] Full article

(This article belongs to the Special Issue Mathematical Modeling of Aortic Diseases)

3 pages, 143 KiB

Open AccessEditorial

Molecular Diagnostics in the Postgenomic Era

by Petra Korać and Maja Matulić

Bioengineering 2024, 11(12), 1259; https://doi.org/10.3390/bioengineering11121259 - 12 Dec 2024

Abstract

Since the end of the 20th century, when the first eukaryotic organism was sequenced, genome sequencing as a technique has made incredible progress, and today new methods allow sequencing different genomes and genes at relatively low prices and in a short time [...] [...] Read more.

Since the end of the 20th century, when the first eukaryotic organism was sequenced, genome sequencing as a technique has made incredible progress, and today new methods allow sequencing different genomes and genes at relatively low prices and in a short time [...] Full article

(This article belongs to the Special Issue Molecular Diagnostics in Postgenomic Era)

17 pages, 4943 KiB

Open AccessArticle

Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically Pumped Magnetometer Magnetoencephalography

by Yuyu Ma, Xiaoyu Liang, Huanqi Wu, Hao Lu, Yong Li, Changzeng Liu, Yang Gao, Min Xiang, Dexin Yu and Xiaolin Ning

Bioengineering 2024, 11(12), 1258; https://doi.org/10.3390/bioengineering11121258 - 12 Dec 2024

Abstract

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In [...] Read more.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks. Full article

(This article belongs to the Section Biosignal Processing)

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14 pages, 3560 KiB

Open AccessArticle

Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images

by Qiye Cheng, Jingyi Zhang, Mengting Hu, Shigeng Wang, Yijun Liu, Jianying Li and Wei Wei

Bioengineering 2024, 11(12), 1257; https://doi.org/10.3390/bioengineering11121257 - 12 Dec 2024

Abstract

The dual-energy spectral CT (DEsCT) employs material decomposition (MD) technology, opening up novel avenues for the opportunistic assessment of bone status. Radiomics, a powerful tool for elucidating the structural and textural characteristics of bone, aids in the detection of mineral loss. Therefore, this [...] Read more.

The dual-energy spectral CT (DEsCT) employs material decomposition (MD) technology, opening up novel avenues for the opportunistic assessment of bone status. Radiomics, a powerful tool for elucidating the structural and textural characteristics of bone, aids in the detection of mineral loss. Therefore, this study aims to compare the efficacy of bone status assessment using both bone density measurements and radiomics models derived from MD images and to further explore the clinical value of radiomics models. Methods: Retrospective data were collected from 307 patients who underwent both quantitative computed tomography (QCT) and full-abdomen DEsCT scans at our institution. Based on QCT measurements, patients were divided into three categories: normal bone mineral density (BMD), osteopenia, and osteoporosis. Using the abdominal DEsCT data, six types of MD images were reconstructed, including HAP (Water), HAP (Fat), Ca (Water), Ca (Fat), Fat (Ca), and Fat (HAP). Patients were randomly divided into a training cohort (n = 214) and a validation cohort (n = 93) at a ratio of 7:3. Focusing on the L1 to L3 vertebrae, density values from the six MD images were measured. Six density value models and six radiomics models were constructed using a random forest (RF) classifier. The performance of these models in assessing bone status was evaluated using the receiver operating characteristic (ROC) curves, and the DeLong test was employed to compare performance differences between the models. Results: The macro-area under the curve (AUC) values for the density value models based on HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images were 0.870, 0.870, 0.847, and 0.765, respectively, which outperformed those of Fat (Ca) (AUC = 0.623) and Fat (HAP) (AUC = 0.618) density value models. In the comparison of radiomics models, the trends of model performance were consistent with the density value models across the six MD images. However, the models based on HAP (Water), Ca (Water), HAP (Fat), Ca (Fat), Fat (Ca), and Fat (HAP) images exhibited superior performance than those of the density value models with the corresponding MD images, with values of 0.946, 0.941, 0.934, 0.926, 0.831, and 0.824, respectively. Conclusions: Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential. Full article

(This article belongs to the Section Biosignal Processing)

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12 pages, 1955 KiB

Open AccessArticle

Automated Assessment of the Pulmonary Artery-to-Ascending Aorta Ratio in Fetal Cardiac Ultrasound Screening Using Artificial Intelligence

by Rina Aoyama, Masaaki Komatsu, Naoaki Harada, Reina Komatsu, Akira Sakai, Katsuji Takeda, Naoki Teraya, Ken Asada, Syuzo Kaneko, Kazuki Iwamoto, Ryu Matsuoka, Akihiko Sekizawa and Ryuji Hamamoto

Bioengineering 2024, 11(12), 1256; https://doi.org/10.3390/bioengineering11121256 - 12 Dec 2024

Abstract

The three-vessel view (3VV) is a standardized transverse scanning plane used in fetal cardiac ultrasound screening to measure the absolute and relative diameters of the pulmonary artery (PA), ascending aorta (Ao), and superior vena cava, as required. The PA/Ao ratio is used to [...] Read more.

The three-vessel view (3VV) is a standardized transverse scanning plane used in fetal cardiac ultrasound screening to measure the absolute and relative diameters of the pulmonary artery (PA), ascending aorta (Ao), and superior vena cava, as required. The PA/Ao ratio is used to support the diagnosis of congenital heart disease (CHD). However, vascular diameters are measured manually by examiners, which causes intra- and interobserver variability in clinical practice. In the present study, we aimed to develop an artificial intelligence-based method for the standardized and quantitative evaluation of 3VV. In total, 315 cases and 20 examiners were included in this study. We used the object-detection software YOLOv7 for the automated extraction of 3VV images and compared three segmentation algorithms: DeepLabv3+, UNet3+, and SegFormer. Using the PA/Ao ratios based on vascular segmentation, YOLOv7 plus UNet3+ yielded the most appropriate classification for normal fetuses and those with CHD. Furthermore, YOLOv7 plus UNet3+ achieved an arithmetic mean value of 0.883 for the area under the receiver operating characteristic curve, which was higher than 0.749 for residents and 0.808 for fellows. Our automated method may support unskilled examiners in performing quantitative and objective assessments of 3VV images during fetal cardiac ultrasound screening. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Segmentation, 2nd Edition)

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15 pages, 3365 KiB

Open AccessArticle

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

by Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui and Ke Sheng

Bioengineering 2024, 11(12), 1255; https://doi.org/10.3390/bioengineering11121255 - 11 Dec 2024

Abstract

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates [...] Read more.

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. The results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of the average dice similarity coefficient (DSC) and the Hausdorff distance (HD95p), except in two mice for intestine contouring. This superior performance is especially evident in the external dataset, confirming the model’s robustness to variations in imaging conditions, including noise and quality, and thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows. Full article

(This article belongs to the Special Issue AI and Data Science in Bioengineering: Innovations and Applications)

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14 pages, 3716 KiB

Open AccessTechnical Note

Mandibular Reconstruction with Osseous Free Flap and Immediate Prosthetic Rehabilitation (Jaw-in-a-Day): In-House Manufactured Innovative Modular Stackable Guide System

by Matthias Ureel, Pieter-Jan Boderé, Benjamin Denoiseux, Pasquier Corthouts and Renaat Coopman

Bioengineering 2024, 11(12), 1254; https://doi.org/10.3390/bioengineering11121254 - 11 Dec 2024

Abstract

Background: Head and neck reconstruction following ablative surgery results in alterations to maxillofacial anatomy and function. These postoperative changes complicate dental rehabilitation. Methods: An innovative modular, stackable guide system for immediate dental rehabilitation during mandibular reconstruction is presented. The virtual surgical planning was [...] Read more.

Background: Head and neck reconstruction following ablative surgery results in alterations to maxillofacial anatomy and function. These postoperative changes complicate dental rehabilitation. Methods: An innovative modular, stackable guide system for immediate dental rehabilitation during mandibular reconstruction is presented. The virtual surgical planning was performed in Materialise Innovation Suite v26 and Blender 3.6 with the Blenderfordental add-on. The surgical guides and models were designed and manufactured at the point of care. Results: The duration of the surgery was 9 h and 35 min. Good implant stability (>35 Ncm) and a stable occlusion were achieved. After 9 months of follow-up, the occlusion remained stable, and a mouth opening of 25 mm was registered. The dental implants showed no signs of peri-implant bone loss. Superposition of the preoperative planning and postoperative position of the fibula parts resulted in an average difference of 0.70 mm (range: −1.9 mm; 5.4 mm). Conclusions: The in-house developed stackable guide system resulted in a predictive workflow and accurate results. The preoperative virtual surgical planning was time-consuming and required extensive CAD/CAM and surgical expertise. The addition of fully guided implant placement to this stackable guide system would be beneficial. More research with longer follow-ups is necessary to validate these results. Full article

(This article belongs to the Special Issue Computer-Assisted Maxillofacial Surgery)

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