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Bioengineering
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New Sights of Deep Learning and Digital Model in Biomedicine
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New Sights of Deep Learning and Digital Model in Biomedicine

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

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 11267

Editor

Dr. John A. St. Cyr

E-Mail Website
Guest Editor
Regulatory & Clinical Research Institute, University of Minnesota, Minneapolis, MN 55455, USA
Interests: cardiovascular surgery; congenital heart disease; heart failure; myocardial metabolism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

This Special Issue explores the transformative impact of deep learning and digital modeling technologies within the field of biomedicine. As artificial intelligence continues to evolve, its applications in healthcare are becoming increasingly sophisticated, promising enhanced diagnostics, personalized treatment strategies, and improved patient outcomes.

It includes, but is not limited to, the following fields:

Deep learning algorithms: innovative approaches using neural networks and machine learning techniques tailored for medical imaging, genomics, and clinical data analysis.

Digital twins in healthcare: the application of digital twin technology to create virtual representations of patients or biological systems, enabling personalized medicine and real-time monitoring.

Predictive analytics: techniques for forecasting disease progression and treatment responses based on historical data, enhancing decision-making processes in clinical settings.

Integration with bioinformatics: utilization of deep learning in the processing of complex biological data, leading to advances in drug discovery and biomarker identification.

Ethics and regulation: considerations surrounding the ethical implications and regulatory challenges posed by the integration of AI in medicinal practices.

Interdisciplinary collaborations: the importance of cross-disciplinary teamwork involving data scientists, clinicians, and biomedical researchers to foster innovation in biomedicine.

The goal is to showcase cutting-edge research and emerging technologies that bridge deep learning and biomedicine.

It will inspire collaborations and dialog among researchers, healthcare professionals, and industry stakeholders.

This Special Issue aims to provide a comprehensive overview of current advancements and ongoing challenges in the application of deep learning and digital modeling in biomedicine, emphasizing the potential to revolutionize healthcare delivery and improve patient care.

Dr. John A. St. Cyr
Guest Editor

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

  • deep learning
  • digital model
  • biomedicine

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Related Special Issue

Published Papers (5 papers)

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Research

18 pages, 611 KB  
Article
Clinically Robust Deep Learning for Contrast-Enhanced Mammography: Multicenter Evaluation Across Convolutional Neural Network Architectures
by Roberta Fusco, Vincenza Granata, Paolo Vallone, Teresa Petrosino, Maria Daniela Iasevoli, Roberta Galdiero, Mauro Mattace Raso, Davide Pupo, Filippo Tovecci, Annamaria Porto, Gerardo Ferrara, Modesta Longobucco, Giulia Capuano, Roberto Morcavallo, Caterina Todisco, Fabiana Antenucci, Mario Sansone, Mimma Castaldo, Daniele La Forgia and Antonella Petrillo
Bioengineering 2026, 13(4), 475; https://doi.org/10.3390/bioengineering13040475 - 17 Apr 2026
Cited by 1 | Viewed by 762
Abstract
Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective [...] Read more.
Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective multicenter study, CEM images from 300 patients (314 lesions) were combined with 1003 publicly available CEM images, yielding a total of 1120 breast cases. Automatic breast segmentation was performed using the LIBRA framework to generate breast-mask images. Eleven deep learning models, including classical convolutional neural networks, attention-based networks, hybrid convolutional neural networks (CNNs), Transformer architectures, and mammography-specific models, were trained and evaluated using both original DICOM images and breast-mask inputs. Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, AUROC, and AUPRC on cross-validation and independent test sets. Hyperparameter optimization was conducted for the best-performing architecture. Results: Models trained on breast-mask images consistently outperformed those trained on original DICOM images across all architectures and metrics, with AUROC improvements ranging from +0.06 to +0.21. Among all models, ResNet50 trained on breast-mask images achieved the best performance (AUROC = 0.931; AUPRC = 0.933; balanced accuracy = 0.834), further improved after optimization (balanced accuracy = 0.886; sensitivity = 0.842; specificity = 0.930). Classical CNN architectures demonstrated performance comparable to or exceeding that of more complex hybrid CNN–Transformer models when anatomically focused preprocessing and rigorous optimization were applied. Conclusions: Anatomically constrained preprocessing through breast-mask segmentation substantially enhances deep learning performance and stability in CEM-based breast lesion classification. These findings indicate that input representation quality and training optimization are critical determinants of clinically relevant performance, often outweighing architectural complexity, and may support more reliable AI-assisted decision support in CEM workflows. Full article
(This article belongs to the Special Issue New Sights of Deep Learning and Digital Model in Biomedicine)
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18 pages, 2683 KB  
Article
Engineering the Image Representation for Deep Learning in Contrast-Enhanced Mammography: A Systematic Analysis of Preprocessing and Anatomical Masking
by Roberta Fusco, Vincenza Granata, Paolo Vallone, Teresa Petrosino, Maria Daniela Iasevoli, Mauro Mattace Raso, Davide Pupo, Piero Trovato, Igino Simonetti, Paolo Pariante, Vincenzo Cerciello, Gerardo Ferrara, Modesta Longobucco, Giulia Capuano, Roberto Morcavallo, Caterina Todisco, Fabiana Antenucci, Mario Sansone, Daniele La Forgia and Antonella Petrillo
Bioengineering 2026, 13(3), 322; https://doi.org/10.3390/bioengineering13030322 - 11 Mar 2026
Cited by 2 | Viewed by 1306
Abstract
Deep-learning models applied to contrast-enhanced mammography (CEM) are known to be highly sensitive to the input image representation. However, preprocessing is often treated as a secondary step and rarely analyzed as an independent design variable. In this work, we present a systematic engineering [...] Read more.
Deep-learning models applied to contrast-enhanced mammography (CEM) are known to be highly sensitive to the input image representation. However, preprocessing is often treated as a secondary step and rarely analyzed as an independent design variable. In this work, we present a systematic engineering analysis of a deterministic, label-independent preprocessing pipeline for CEM images. The pipeline integrates intensity normalization, global histogram matching, local contrast enhancement, denoising, and anatomically constrained breast masking. Using a controlled experimental design, identical deep-learning architectures were trained under different input representations to isolate the impact of preprocessing on classification performance and stability. Across convolutional neural network architectures, anatomically constrained preprocessing consistently improves discrimination performance, reduces variability across cross-validation folds, and enhances training stability. Breast mask-based representations demonstrate substantial gains in AUROC and AUPRC compared to raw DICOM inputs. These findings highlight image preprocessing as a first-class engineering component in medical AI pipelines. Breast masking significantly improves robustness and generalization, independently of network architecture complexity. From a clinical perspective, improving model robustness and sensitivity to malignant lesions may contribute to more reliable AI-assisted decision support in contrast-enhanced mammography, particularly in settings characterized by acquisition variability and heterogeneous patient populations. Full article
(This article belongs to the Special Issue New Sights of Deep Learning and Digital Model in Biomedicine)
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21 pages, 4855 KB  
Article
ICIsc: A Deep Learning Framework for Predicting Immune Checkpoint Inhibitor Response by Integrating scRNA-Seq and Protein Language Models
by Zhenyu Jin, Di Zhang and Luonan Chen
Bioengineering 2026, 13(2), 187; https://doi.org/10.3390/bioengineering13020187 - 6 Feb 2026
Viewed by 1557
Abstract
Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for [...] Read more.
Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms. Full article
(This article belongs to the Special Issue New Sights of Deep Learning and Digital Model in Biomedicine)
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21 pages, 1471 KB  
Article
The PIEE Cycle: A Structured Framework for Red Teaming Large Language Models in Clinical Decision-Making
by Maissa Trabilsy, Srinivasagam Prabha, Cesar A. Gomez-Cabello, Syed Ali Haider, Ariana Genovese, Sahar Borna, Nadia Wood, Narayanan Gopala, Cui Tao and Antonio J. Forte
Bioengineering 2025, 12(7), 706; https://doi.org/10.3390/bioengineering12070706 - 27 Jun 2025
Cited by 4 | Viewed by 2648
Abstract
The increasing integration of large language models (LLMs) into healthcare presents significant opportunities, but also critical risks related to patient safety, accuracy, and ethical alignment. Despite these concerns, no standardized framework exists for systematically evaluating and stress testing LLM behavior in clinical decision-making. [...] Read more.
The increasing integration of large language models (LLMs) into healthcare presents significant opportunities, but also critical risks related to patient safety, accuracy, and ethical alignment. Despite these concerns, no standardized framework exists for systematically evaluating and stress testing LLM behavior in clinical decision-making. The PIEE cycle—Planning and Preparation, Information Gathering and Prompt Generation, Execution, and Evaluation—is a structured red-teaming framework developed specifically to address artificial intelligence (AI) safety risks in healthcare decision-making. PIEE enables clinicians and informatics teams to simulate adversarial prompts, including jailbreaking, social engineering, and distractor attacks, to stress-test language models in real-world clinical scenarios. Model performance is evaluated using specific metrics such as true positive and false positive rates for detecting harmful content, hallucination rates measured through adapted TruthfulQA scoring, safety and reliability assessments, bias detection via adapted BBQ benchmarks, and ethical evaluation using structured Likert-based scoring rubrics. The framework is illustrated using examples from plastic surgery, but is adaptable across specialties, and is intended for use by all medical providers, regardless of their backgrounds or familiarity with artificial intelligence. While the framework is currently conceptual and validation is ongoing, PIEE provides a practical foundation for assessing the clinical reliability and ethical robustness of LLMs in medicine. Full article
(This article belongs to the Special Issue New Sights of Deep Learning and Digital Model in Biomedicine)
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15 pages, 9787 KB  
Article
Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System
by Xiayue Xu, Boxiang Yun, Yumin Zhao, Ling Jin, Yanning Zong, Guanzhen Yu, Chuanliang Zhao, Kai Fan, Xiaolin Zhang, Shiwang Tan, Zimu Zhang, Yan Wang, Qingli Li and Shaoqing Yu
Bioengineering 2025, 12(1), 10; https://doi.org/10.3390/bioengineering12010010 - 25 Dec 2024
Cited by 3 | Viewed by 3776
Abstract
Objective: We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery. Methods: We retrospectively analyzed 1050 video data [...] Read more.
Objective: We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery. Methods: We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm. Results: The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask’s segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5. Conclusions: This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery. Full article
(This article belongs to the Special Issue New Sights of Deep Learning and Digital Model in Biomedicine)
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