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Bioengineering
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AI and Data Science in Biomedicine: Powering the Next Generation...
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AI and Data Science in Biomedicine: Powering the Next Generation of Diagnostics and Therapies

A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4407

Editors

Dr. Hongqing Yu

E-Mail Website
Guest Editor
School of Computing and Human Sciences Research Centre, University of Derby, Derby DE22 3AW, UK
Interests: data science; machine learning; knowledge discovery and representation; semantic technologies; deep machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yifan Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Mechanics, Eastern Institute of Technology, Ningbo 315200, China
Interests: deep learning; computer vision; digital twinning; sensor technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) and Data Science into biomedicine has emerged as a transformative force, reshaping traditional paradigms of disease diagnostics, treatment methodologies, and patient care. As healthcare challenges grow increasingly complex, the need for innovative solutions that harness the power of large datasets, sophisticated algorithms, and computational models has never been more urgent. This Special Issue of Bioengineering aims to explore the convergence of AI, data science, and biomedicine, illuminating novel applications and profound implications for healthcare professionals, researchers, and patients alike.

AI and data science offer unprecedented opportunities to enhance the efficiency and accuracy of biomedicine. Machine learning techniques, particularly in deep learning, have shown remarkable capability for processing vast amounts of biomedical data—ranging from genomic sequences to clinical imaging. By analyzing these data types, AI can identify patterns and insights that are often imperceptible to human experts, leading to more accurate diagnostic tools, personalized treatment plans, and predictive models that anticipate patient outcomes. The continuous advancement in AI technologies provides a fertile ground for innovation that can redefine therapeutic strategies across various medical disciplines.

This Special Issue seeks to present cutting-edge research, reviews, and perspectives that highlight the applications of AI and data science in biomedicine. We invite contributions that demonstrate how AI and data science are actively engineering the future of biomedicine, turning complex data into actionable knowledge, and ultimately, into more precise, predictive, and personalized healthcare solutions for all.

Dr. Hongqing Yu
Prof. Dr. Yifan Zhao
Guest Editors

Dr. Stylianos Didaskalou
Guest Editor Assistant
Email:
Institute for Language and Speech Processing, Athena Research Center, 67100 Panepistimioupoli, Greece
Interests: biomedical image and signal processing; biomedical physics; advanced fluorescence microscopy techniques

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in biomedicine
  • data science
  • machine learning
  • diagnostics
  • personalized medicine
  • predictive analytics

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Published Papers (6 papers)

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Research

36 pages, 1205 KB  
Article
MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients
by Bassem Jandoubi and Moulay A. Akhloufi
Bioengineering 2026, 13(9), 993; https://doi.org/10.3390/bioengineering13090993 - 27 Aug 2026
Viewed by 342
Abstract
Cardiovascular deterioration is a clinically important event in intensive care units, but early prediction remains challenging because risk may be reflected across structured clinical variables, physiological time series, and clinical text. In this work, we present MultiCardioNet, a multimodal deep learning framework for [...] Read more.
Cardiovascular deterioration is a clinically important event in intensive care units, but early prediction remains challenging because risk may be reflected across structured clinical variables, physiological time series, and clinical text. In this work, we present MultiCardioNet, a multimodal deep learning framework for early prediction of a composite ICU deterioration endpoint using MIMIC-IV data. The prediction target was defined as vasopressor initiation and/or early death during the 24–72 h outcome window, making the task broader than mortality prediction alone but also related to treatment escalation and circulatory support. The model combines structured clinical variables, 24-h vital sign time series, and timestamp-filtered radiology reports. Structured information was represented using enriched first-24-h clinical features, while physiological dynamics were modeled using a transformer-based time series encoder and radiology reports were represented using CXR-BERT-specialized embeddings. On the held-out test set, MultiCardioNet achieved an AUROC of 0.9014, AUPRC of 0.8481, and F1-score of 0.7723. These findings suggest that the three modalities provide complementary information for this composite deterioration endpoint in the internal test setting. 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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21 pages, 1747 KB  
Article
Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool–Tissue Force Prediction from Laparoscopic Depth Maps
by Fabiano Bini, Alessia Finti, Guido Manni and Franco Marinozzi
Bioengineering 2026, 13(8), 863; https://doi.org/10.3390/bioengineering13080863 - 26 Jul 2026
Viewed by 523
Abstract
Physically consistent estimation of soft-tissue mechanical properties is critical for surgical robotics, intraoperative safety monitoring, and simulator initialization, yet existing methods typically require force-sensing hardware or manual parameter tuning. This paper presents a physics-informed generative framework that estimates tissue stiffness (ks [...] Read more.
Physically consistent estimation of soft-tissue mechanical properties is critical for surgical robotics, intraoperative safety monitoring, and simulator initialization, yet existing methods typically require force-sensing hardware or manual parameter tuning. This paper presents a physics-informed generative framework that estimates tissue stiffness (ks), damping coefficient (kd), and tool–tissue contact force magnitude (Fmag) from monocular laparoscopic video in a label-free manner with respect to mechanical parameters and interaction forces. The pipeline integrates three components: DepthPro, a multi-scale Vision Transformer (ViT) for zero-shot metric depth estimation; a 3D geometric contact detection pipeline; and a dual-mode conditional generative network trained via a five-term physics–adversarial loss. A differentiable Mass–Spring–Damper (MSD) simulator is embedded directly in the training loop. This enables gradient-based parameter learning without force-sensor, displacement, or boundary-condition supervision. Parameter identifiability is supported through dual observational grounding: MSD physics consistency against observed contact displacement, and next-frame depth map reconstruction. Validated on CholecSeg8k cholecystectomy sequences, Physics-Informed Neural Network (PINN)-estimated parameters significantly outperform static literature baselines (Wilcoxon p = 2.49 × 10−8, Cohen’s d = 0.374), with physically plausible viscoelastic settling dynamics recovered within 0.9 s of tool release. Since no force sensors were present at acquisition time, evaluation follows an indirect simulation-consistency protocol. Mechanical parameters are estimated at 1.6 ms/frame, a negligible addition to the monocular depth front end that sets the pipeline rate. Estimated parameters directly enable stiffness-aware haptic rendering, intraoperative safety monitoring, and scene-adapted surgical simulation initialization. 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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11 pages, 1365 KB  
Article
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
Viewed by 531
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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33 pages, 4925 KB  
Article
ACross-Paradigm CNN–Swin Transformer Ensemble with Super-Resolution Enhancement for Multi-Class Alzheimer’s Disease Classification
by Mohamed H. Habeb, Reem A. Alnanih and Lamiaa A. Elrefaei
Bioengineering 2026, 13(6), 666; https://doi.org/10.3390/bioengineering13060666 - 8 Jun 2026
Viewed by 634
Abstract
Alzheimer’s disease (AD) is a global health challenge requiring early and accurate diagnosis, yet current clinical methods struggle with early stages. Deep learning approaches for MRI-based diagnosis face persistent challenges related to image quality issues, limited model generalization, and subtle inter-class variations. To [...] Read more.
Alzheimer’s disease (AD) is a global health challenge requiring early and accurate diagnosis, yet current clinical methods struggle with early stages. Deep learning approaches for MRI-based diagnosis face persistent challenges related to image quality issues, limited model generalization, and subtle inter-class variations. To address these limitations, this paper proposes a robust, end-to-end brain MRI-based framework for multi-class classification of AD stages. Positioned within the broader research priority of artificial intelligence and intelligent healthcare technologies, the proposed methodology incorporates an attention-based ensemble of deep learning models alongside an enhanced image preprocessing that uses Real-ESRGAN to mitigate common compression and resolution degradations in 2-D MRI slices. The ensemble makes use of the superior capabilities of the Swin Transformer to capture global contextual dependencies and EfficientNet-B3/MobileNetV2 for effective multi-scale feature extraction, with feature fusion performed using a Squeeze-and-Excitation attention mechanism. The experiments were performed on a publicly available Alzheimer’s MRI dataset, resulting in classification accuracy of 94.47% and 92.28% for the two proposed frameworks. The robustness and clinical interpretability of the framework are emphasized through comprehensive metrics and qualitative analysis. This framework demonstrates promising benchmark performance on a standardized public dataset, highlighting the potential of cross-paradigm ensembles combined with super-resolution preprocessing. 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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20 pages, 5525 KB  
Article
Predictions of Oncotype DX® High-Risk Classification Using Magnetic Resonance Imaging-Based Intratumoral Heterogeneity
by Sung Joon Park, Won Hwa Kim, Jaeil Kim, Taewoo Kang, Ji-Young Park, Byeongju Kang, Joon Suk Moon, Ho Yong Park, Hye Jung Kim and Jeeyeon Lee
Bioengineering 2026, 13(6), 611; https://doi.org/10.3390/bioengineering13060611 - 24 May 2026
Viewed by 717
Abstract
The Oncotype DX® 21-gene recurrence score (RS) guides adjuvant chemotherapy decisions in estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2−) breast cancer, yet requires invasive tissue sampling and involves substantial costs. This study evaluated intratumoral tumor ecological diversity (iTED), a habitat [...] Read more.
The Oncotype DX® 21-gene recurrence score (RS) guides adjuvant chemotherapy decisions in estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2−) breast cancer, yet requires invasive tissue sampling and involves substantial costs. This study evaluated intratumoral tumor ecological diversity (iTED), a habitat imaging approach, as a non-invasive complement for predicting Oncotype DX® high-risk classification (RS > 25). This retrospective multi-center study included 312 patients with ER+/HER2− invasive breast cancer who underwent Oncotype DX® testing (development: n = 168; external validation: n = 144). The iTED framework employed superpixel-based habitat determination using Gaussian mixture models on pretreatment dynamic contrast-enhanced MRI. Four predictive models were compared: clinical, conventional whole-tumor radiomics (C-radiomics), iTED, and combined (Clinical + iTED). The iTED model achieved higher discriminative performance compared with C-radiomics in both development (area under the curve [AUC]: 0.868 ± 0.068 vs. 0.730 ± 0.112) and external validation (AUC: 0.811 vs. 0.587) sets. The combined model further improved performance (development AUC: 0.908 ± 0.043; external AUC: 0.889). Habitat imaging-based iTED features achieved numerically higher performance than conventional radiomics in predicting Oncotype DX® high-risk classification. These findings suggest the potential of iTED as a non-invasive imaging biomarker to support molecular testing in clinical decision-making. 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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20 pages, 8054 KB  
Article
Quantifying the Contribution of Bone Morphology to Implant Selection in Shoulder Arthroplasty Using CT-Based Deep Learning
by Andrea Moglia, Luca Marsilio, Matteo Rossi, Alfonso Manzotti, Luca Mainardi and Pietro Cerveri
Bioengineering 2026, 13(5), 574; https://doi.org/10.3390/bioengineering13050574 - 19 May 2026
Viewed by 501
Abstract
We investigated whether bone morphology alone can inform implant selection in shoulder arthroplasty using a hypothesis-driven deep learning framework applied to preoperative computed tomography (CT) scans. The proposed approach extends a previously validated segmentation and pathology-staging pipeline by introducing implant-type prediction and a [...] Read more.
We investigated whether bone morphology alone can inform implant selection in shoulder arthroplasty using a hypothesis-driven deep learning framework applied to preoperative computed tomography (CT) scans. The proposed approach extends a previously validated segmentation and pathology-staging pipeline by introducing implant-type prediction and a controlled human–AI comparison. The workflow combines CEL-UNet for 3D bone segmentation with ArthroNet+, a multi-task network assessing osteophytes, joint-space narrowing, humeroscapular alignment, and implant type. Trained on a multicenter cohort of 600 patients, CEL-UNet achieved Dice scores of 0.99 for the humerus and 0.98 for the scapula. ArthroNet+ achieved high performance in pathology classification (up to 95% for alignment tasks). Under morphology-only conditions, ten orthopedic surgeons achieved 61% accuracy with low inter-rater agreement (Fleiss’ κ≈0.15), while the model reached 78% agreement with the implant choices observed in the dataset, reflecting the ability to reproduce clinical decision patterns rather than to identify an optimal implant selection. This performance is characterized by a class-dependent asymmetry, with higher recall for reverse implants than for anatomical ones. These findings indicate that bone morphology provides a measurable but incomplete signal for implant selection, and should therefore not be interpreted as reflecting clinical decision-making performance. The framework quantifies the morphology-driven component of surgical decision making under controlled conditions, supporting future integration with multimodal clinical data. 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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