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

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 18304

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Dr. Muneer Ahmad

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Department of Computer Science, University of Roehampton, Roehampton Lane SW15 5 PH, UK
Interests: artificial intelligence; smart healthcare; disease diagnosis; drug discovery; clinical decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) and data science into bioengineering has the potential to revolutionize the field by providing new data analysis and methods predictive modeling and the development of innovative bioengineering solutions. This Special Issue aims to explore the intersection of AI, data science, and bioengineering, showcasing how these technologies can advance bioengineering science and technology by

AI in biomedical engineering and applications;
AI-driven diagnostic tools and medical imaging;
Machine learning algorithms for predictive modeling in clinical engineering;
AI applications in biomechatronic and biomedical devices;
Development of smart biomaterials using AI;
Data science in biomolecular, cellular, and tissue engineering;
Big data analytics in tissue engineering and regenerative medicine;
Data-driven approaches to genetic and metabolic engineering;
Computational modeling and simulation of cellular and molecular processes;
Applications of AI in synthetic biology and bio-nanotechnology;
Bioinformatics and bioprocess engineering;
AI and machine learning for bioinformatics and genomic data analysis;
Data science techniques for optimizing bioprocess design and biocatalysis;
Predictive modeling and simulation in bioreactor design;
Integration of AI in bioseparation and bioenergy production processes;
Wearable and implantable bioelectronics;
AI-enhanced wearable electronics for health monitoring;
Machine learning in implantable electronic devices;
Data-driven design of bioelectronics devices;
Advanced sensing and circuit technologies using AI;
Translational bioengineering;
Case studies demonstrating the translational impact of AI in bioengineering;
AI methodologies for bridging experimental research and clinical applications;
Data science techniques in the development of translational bioengineering solutions;
Innovations in neurotechnology and rehabilitation engineering using AI.

Dr. Muneer Ahmad
Guest Editor

Manuscript Submission Information

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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.

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Keywords

AI
data science
bioengineering
biomedical engineering
predictive modeling
machine learning
smart biomaterials
tissue engineering

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Related Special Issue

AI and Data Science in Bioengineering: Innovations and Applications—Second Edition in Bioengineering

Published Papers (10 papers)

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25 pages, 460 KB

Open AccessArticle

Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support

by Muhammad Zohaib Khan, Shaukat Wasi, Muhammad Shoaib Siddiqui, Ghufran Ahmed, Muhammad Hussain Mughal and Mohsin Iftikhar

Bioengineering 2026, 13(8), 929; https://doi.org/10.3390/bioengineering13080929 - 17 Aug 2026

Viewed by 401

Abstract

Early identification of COVID-19 patients requiring intensive care is critical for improving treatment prioritization, supporting clinical decision-making, and managing limited hospital resources. While structured electronic health record (EHR) data provide important physiological information, chest computed tomography (CT) imaging contains additional indicators related to disease severity. This study presents a multimodal clinical decision support framework for a multimodal risk prediction framework for severe COVID-19 outcomes using structured emergency clinical features and patient-level CT imaging from the COVID Data for Shared Learning (CDSL) dataset. After multimodal cohort construction, 784 patients with both structured clinical records and CT imaging were included in the analysis. Three predictive settings were evaluated: EHR-only prediction using Gradient Boosting, CT-only prediction using ResNet50-based feature extraction with Logistic Regression, and multimodal prediction using weighted late fusion. The experimental results indicate that the CT-based model surpassed the clinical baseline, yielding an F1-score of 0.42 and an ROC-AUC of 0.772, whereas the EHR-only model achieved scores of 0.30 and 0.715, respectively. Overall, the multimodal fusion framework achieved the strongest results among the approaches tested, reaching an F1-score of 0.47 and an ROC-AUC of 0.782. Taken together, these findings indicate that, although CT imaging alone carries meaningful predictive power for evaluating ICU risk, combining it with clinical data leads to predictions that are more reliable and robust. The proposed framework offers a practical and interpretable foundation for multimodal clinical decision support and demonstrates the potential of combining structured clinical data with medical imaging for intelligent critical care applications. Full article

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

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24 pages, 55994 KB

Open AccessArticle

A Method for Workout Video Classification via Explainable and Federated Learning

by Ludovica Ciardiello, Patrizia Agnello, Marta Petyx, Fabio Martinelli, Mario Cesarelli, Antonella Santone and Francesco Mercaldo

Bioengineering 2026, 13(6), 603; https://doi.org/10.3390/bioengineering13060603 - 22 May 2026

Viewed by 424

Abstract

In recent years, the widespread availability of wearable devices and smartphones has enabled the large-scale collection of human activity data, fostering new opportunities for automatic workout recognition and personalized fitness monitoring. However, the centralized storage of video recordings raises critical privacy concerns, particularly when raw data contain identifiable individuals. Federated Machine Learning provides a paradigm designed with the aim of reducing privacy risks; here, models are collaboratively trained across distributed clients without sharing their sensitive data. In this paper, we propose an approach for workout video classification with Federated Machine Learning, enhanced by explainability through Gradient-weighted Class-Activation Mapping. The proposed method is evaluated on a real-world multi-class exercise video dataset, organized into eight biomechanically coherent macro-classes. In the experimental analysis, we consider several federated configurations in terms of the number of clients, the chosen aggregation strategy, and global communication rounds. The obtained results demonstrate that different aggregation strategies achieve comparable overall accuracy, while explainability effectively highlights the discriminative regions associated with exercise execution, revealing meaningful differences in model behavior between aggregation strategies and uncovering misclassifications driven by contextual biases, demonstrating the trustworthiness of the proposed approach for explainable workout video classification. Full article

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

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18 pages, 12952 KB

Open AccessArticle

Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks

by Pei-Yu Lin, Yidan Shen, Neville Mathew, Renjie Hu, Siyu Huang, Courtney M. Queen, Cameron E. West, Ana Ciurea and George Zouridakis

Bioengineering 2026, 13(2), 245; https://doi.org/10.3390/bioengineering13020245 - 20 Feb 2026

Cited by 1 | Viewed by 1652

Abstract

Melanoma is the most lethal form of skin cancer, and early detection is critical for improving patient outcomes. Although dermoscopy combined with deep learning has advanced automated skin-lesion analysis, progress is hindered by limited access to large, well-annotated datasets and by severe class imbalance, where melanoma images are substantially underrepresented. To address these challenges, we present the first systematic benchmarking study comparing four GAN architectures—DCGAN, StyleGAN2, and two StyleGAN3 variants (T and R)—for high-resolution (

512 \times 512

) melanoma-specific synthesis. We train and optimize all models on two expert-annotated benchmarks (ISIC 2018 and ISIC 2020) under unified preprocessing and hyperparameter exploration, with particular attention to R1 regularization tuning. Image quality is assessed through a multi-faceted protocol combining distribution-level metrics (FID), sample-level representativeness (FMD), qualitative dermoscopic inspection, downstream classification with a frozen EfficientNet-based melanoma detector, and independent evaluation by two board-certified dermatologists. StyleGAN2 achieves the best balance of quantitative performance and perceptual quality, attaining FID scores of 24.8 (ISIC 2018) and 7.96 (ISIC 2020) at

γ = 0.8

. The frozen classifier recognizes 83% of StyleGAN2-generated images as melanoma, while dermatologists distinguish synthetic from real images at only 66.5% accuracy (chance = 50%), with low inter-rater agreement (

κ = 0.17

). In a controlled augmentation experiment, adding synthetic melanoma images to address class imbalance improved melanoma detection AUC from 0.925 to 0.945 on a held-out real-image test set. These findings demonstrate that StyleGAN2-generated melanoma images preserve diagnostically relevant features and can provide a measurable benefit for mitigating class imbalance in melanoma-focused machine learning pipelines. Full article

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

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14 pages, 250 KB

Open AccessArticle

Exploring an AI-First Healthcare System

by Ali Gates, Asif Ali, Scott Conard and Patrick Dunn

Bioengineering 2026, 13(1), 112; https://doi.org/10.3390/bioengineering13010112 - 17 Jan 2026

Cited by 5 | Viewed by 2438

Abstract

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

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

19 pages, 5487 KB

Open AccessArticle

Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages

by Su-Yeon Hwang, Tae-Il Kang, Hyeon-Seo Kim, Seokmin Hong, Jong-Oh Park and Byungjeon Kang

Bioengineering 2026, 13(1), 76; https://doi.org/10.3390/bioengineering13010076 - 9 Jan 2026

Viewed by 1295

Abstract

Magnetic nanoparticles (MNPs) are widely applied in biomedicine, including bioimaging, drug delivery, and cell tracking. As central mediators of immune surveillance, macrophages phagocytize foreign substances, rendering their interactions with MNPs particularly consequential. During MNP uptake, macrophages undergo cytoplasmic remodeling that can lead to morphological alterations. Although prior studies have predominantly focused on MNP uptake efficiency and cytotoxicity, systematic quantitative assessments of macrophage morphological alterations following MNP treatment remain scarce. In this study, phase-contrast microscopy images of macrophages before and after MNP treatment were analyzed using unsupervised variational autoencoder (VAE)-based frameworks. Specifically, the β-VAE, β-total correlation VAE, and multi-encoder VAE frameworks were employed to extract latent representations of cellular morphology. The analysis revealed that MNP-treated macrophages exhibited pronounced structural alterations, including membrane expansion, central density shifts, and shape distortions. These findings were further substantiated through quantitative evaluations, including effect size analysis, kernel density estimation, latent traversal, and difference mapping. Collectively, these results demonstrate that VAE-based unsupervised learning provides a robust framework for detecting subtle morphological responses of macrophages to nanoparticle exposure and highlights its broader applicability across varied cell types, treatment conditions, and imaging platforms. Full article

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

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22 pages, 4171 KB

Open AccessArticle

Evaluation of Subcutaneous and Intermuscular Adipose Tissues by Application of Pattern Recognition and Neural Networks to Ultrasonic Data: A Model Study

by Alexey Tatarinov, Aleksandrs Sisojevs, Vladislavs Agarkovs and Jegors Lukjanovs

Bioengineering 2025, 12(12), 1373; https://doi.org/10.3390/bioengineering12121373 - 17 Dec 2025

Viewed by 951

Abstract

Distinguishing subcutaneous adipose tissue (SAT) from intermuscular adipose tissue (IMAT) is clinically important because IMAT infiltration is strongly associated with age-related functional decline, sarcopenia, diabetes, cardiovascular disease, and obesity. Current assessments rely on MRI or CT, which are stationary, costly, and labor-intensive. Portable ultrasound-based solutions could enable broader, proactive screening. This model study investigated the feasibility of differentially assessing SAT and IMAT using features extracted from propagating ultrasound signals. Twenty-five phantoms were constructed using gelatin as a muscle-mimicking matrix and oil as the SAT and IMAT compartments, arranged to provide gradual variations in fat fractions ranging from 0% to 50%. Ultrasound measurements were collected at 0.8 MHz and 2.2 MHz, and multiple evaluation criteria were computed, including ultrasound velocity and parameters derived from the signal intensity. Classification domains were then generated from intersecting decision rules associated with these criteria. In parallel, artificial neural networks (ANN/LSTM) were trained and tested on identical phantom subsets to evaluate data-driven classification performance. Both the rule-based and ANN/LSTM approaches achieved diagnostically meaningful separation of SAT and IMAT. The aim of this work was to perform an experimental proof-of-concept study on idealized tissue models to demonstrate that ultrasound measurements can reliably differentiate SAT and IMAT, supporting the development of future screening devices. Full article

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

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43 pages, 3034 KB

Open AccessArticle

Real-Time Recognition of NZ Sign Language Alphabets by Optimal Use of Machine Learning

by Mubashir Ali, Seyed Ebrahim Hosseini, Shahbaz Pervez and Muneer Ahmad

Bioengineering 2025, 12(10), 1068; https://doi.org/10.3390/bioengineering12101068 - 30 Sep 2025

Cited by 5 | Viewed by 1592 | Correction

Abstract

The acquisition of a person’s first language is one of their greatest accomplishments. Nevertheless, being fluent in sign language presents challenges for many deaf students who rely on it for communication. Effective communication is essential for both personal and professional interactions and is critical for community engagement. However, the lack of a mutually understood language can be a significant barrier. Estimates indicate that a large portion of New Zealand’s disability population is deaf, with an educational approach predominantly focused on oralism, emphasizing spoken language. This makes it essential to bridge the communication gap between the general public and individuals with speech difficulties. The aim of this project is to develop an application that systematically cycles through each letter and number in New Zealand Sign Language (NZSL), assessing the user’s proficiency. This research investigates various machine learning methods for hand gesture recognition, with a focus on landmark detection. In computer vision, identifying specific points on an object—such as distinct hand landmarks—is a standard approach for feature extraction. Evaluation of this system has been performed using machine learning techniques, including Random Forest (RF) Classifier, k-Nearest Neighbours (KNN), AdaBoost (AB), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), and Logistic Regression (LR). The dataset used for model training and testing consists of approximately 100,000 hand gesture expressions, formatted into a CSV dataset for model training. Full article

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

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15 pages, 3365 KB

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

Cited by 4 | Viewed by 5004

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 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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29 pages, 3485 KB

Open AccessSystematic Review

Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics

by Selma Mtoor, Niki Rashidian, Nouredin Messaoudi, Vincent Grasso, Floriane Noel, Michele Steindler, Derar Jaradat, Isabella Frigerio, Giovanni Butturini, Roland Croner, Karol Rawicz-Pruszynski, Giulia Capelli, Gaya Spolverato, Marc G. Besselink, Takeaki Ishizawa, Elie Chouillard, Mohammad Abu-Hilal, Ulf Kahlert, Ibrahim Dagher and Andrew A. Gumbs

Bioengineering 2026, 13(1), 117; https://doi.org/10.3390/bioengineering13010117 - 20 Jan 2026

Cited by 6 | Viewed by 2130

Abstract

Background: Multimodal AI integration across genomics, radiomics, and pathomics is rapidly evolving in oncology, but evidence remains heterogeneous and unevenly distributed across modalities. Objective: To map empirical studies integrating two or more -omic modalities, summarize integration and validation approaches, and identify gaps informing future directions toward surgomics. Methods: We conducted a scoping review in accordance with PRISMA-ScR, searching PubMed, Ovid, Wiley Online Library, and Google Scholar for English-language studies published from January 2020 to 5 March 2025. We charted study characteristics, modalities combined, fusion strategies, AI model categories, validation approaches, and reported performance metrics as presented by the original studies. Results: From 184 records, 11 studies met inclusion criteria (n = 1078 total participants across reported studies), most focusing on radiomics–pathomics integration; fewer incorporated genomics, and tri-modal fusion was uncommon. Studies varied widely in clinical tasks, endpoints, preprocessing, and validation, limiting direct comparability. Conclusions: The mapped evidence indicates growing methodological activity in radiopathomics and cross-scale association modeling, while tri-modal pipelines and clinically deployable multimodal workflows remain underdeveloped. Surgomics is presented as a conceptual, staged roadmap informed by these gaps rather than a current clinical capability. Full article

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

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1 pages, 127 KB

Open AccessCorrection

Correction: Ali et al. Real-Time Recognition of NZ Sign Language Alphabets by Optimal Use of Machine Learning. Bioengineering 2025, 12, 1068

by Mubashir Ali, Seyed Ebrahim Hosseini, Shahbaz Pervez and Muneer Ahmad

Bioengineering 2026, 13(1), 68; https://doi.org/10.3390/bioengineering13010068 - 7 Jan 2026

Viewed by 526

Abstract

Error in Author List [...] Full article

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