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Bioengineering
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Medical Artificial Intelligence and Data Analysis
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Medical Artificial Intelligence and Data Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 5873

Special Issue Editors

Dr. Ujjwal Baid

E-Mail Website
Guest Editor
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, USA
Interests: artificial intelligence; algorithms; image analysis; computational imaging; tumor segmentation; biomedical images
Dr. Bhakti Baheti

E-Mail Website
Guest Editor
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, USA
Interests: computer vision; machine learning; convolutional neural networks (CNNs); medical image analysis; image classification and segmentation

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technology has been widely used in medical image processing to screen clinical lesions. The classification, segmentation, and location of clinical lesions using medical images represent major tasks for deep learning. In addition, medical image registration based on deep learning constitutes an important research direction.

This Special Issue entitled “Medical Artificial Intelligence and Data Analysis” aims to highlight the most recent advances in the field of deep learning for medical image processing. We invite authors to submit original research articles and review articles that focus on (but are not limited to) the following topics:

1. Advanced deep-learning methods for medical image classification, segmentation, and registration;

2. Lesion identification and localization in medical images;

3. Deep learning in multimodal medical data (including medical images);

4. The interpretability of deep learning for medical images;

5. Deep learning in unbalanced samples for medical images;

6. Efficient annotation of medical images for deep learning;

7. Small-sample learning for medical images;

8. Quality control stress test for deep learning in medical images.

Dr. Ujjwal Baid
Dr. Bhakti Baheti
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

  • artificial intelligence
  • deep learning
  • medical image classification
  • medical image segmentation
  • medical image registration
  • lesion identification and localization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

11 pages, 832 KiB  
Article
Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality
by Vahe S. Panossian, Yu Ma, Bolin Song, Jefferson A. Proaño-Zamudio, Veerle P. C. van Zon, Ikemsinachi C. Nzenwa, Azadeh Tabari, George C. Velmahos, Haytham M. A. Kaafarani, Dimitris Bertsimas and Dania Daye
Bioengineering 2025, 12(4), 336; https://doi.org/10.3390/bioengineering12040336 - 24 Mar 2025
Viewed by 372
Abstract
Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital mortality. Methods: OPTs were trained on patients [...] Read more.
Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital mortality. Methods: OPTs were trained on patients with blunt splenic injuries in the ACS-TQIP 2013–2019 to prescribe one of the three interventions: splenectomy, angioembolization (AE), or observation. Prescriptive trees were derived in two separate patient cohorts: those who presented with a systolic blood pressure (SBP) < 70 mmHg and those with an SBP ≥ 70 mmHg. Splenic injury severity was graded using the American Association of Surgical Trauma (AAST) grading scale. Counterfactual estimation was used to predict the effects of interventions on overall in-hospital mortality. Results: Among 54,345 patients, 3.1% underwent splenic AE, 13.1% splenectomy, and 83.8% were managed with observation. In patients with SBP < 70 mmHg, AE was recommended for shock index (SI) < 1.5 or without transfusion, while splenectomy was indicated for SI ≥ 1.5 with transfusion. For patients with SBP ≥ 70 mmHg, AE was recommended for AAST grades 4–5, or grades 1–3 with SI ≥ 1.2; observation was recommended for grades 1–3 with SI < 1.2. Predicted mortality using OPT-prescribed treatments was 18.4% for SBP < 70 mmHg and 4.97% for SBP ≥ 70 mmHg, compared to observed rates of 36.46% and 7.60%, respectively. Conclusions: Interpretable AI models may serve as a decision aid to improve mortality in patients presenting with a blunt splenic injury. Our data-driven prescriptive OPT models may aid in prescribing the appropriate management in this patient cohort based on their characteristics. Full article
(This article belongs to the Special Issue Medical Artificial Intelligence and Data Analysis)
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17 pages, 2228 KiB  
Article
Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices
by Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Abror Shavkatovich Buriboev, Azizjon Meliboev, Bahodir Muminov and Heung Seok Jeon
Bioengineering 2025, 12(1), 62; https://doi.org/10.3390/bioengineering12010062 - 13 Jan 2025
Cited by 2 | Viewed by 1463
Abstract
The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy [...] Read more.
The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (APS: 14.3) and large tumor localization (APL: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools. Full article
(This article belongs to the Special Issue Medical Artificial Intelligence and Data Analysis)
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20 pages, 3585 KiB  
Article
Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation
by Kuldashboy Avazov, Sanjar Mirzakhalilov, Sabina Umirzakova, Akmalbek Abdusalomov and Young Im Cho
Bioengineering 2024, 11(12), 1302; https://doi.org/10.3390/bioengineering11121302 - 23 Dec 2024
Cited by 6 | Viewed by 1804
Abstract
Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead [...] Read more.
Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes. To address these challenges, this paper proposes a novel modification to the U-Net architecture by integrating a spatial attention mechanism designed to dynamically focus on relevant regions within MRI scans. This innovation enhances the model’s ability to delineate fine tumor boundaries and improves segmentation precision. Our model was evaluated on the Figshare dataset, which includes annotated MRI images of meningioma, glioma, and pituitary tumors. The proposed model achieved a Dice similarity coefficient (DSC) of 0.93, a recall of 0.95, and an AUC of 0.94, outperforming existing approaches such as V-Net, DeepLab V3+, and nnU-Net. These results demonstrate the effectiveness of our model in addressing key challenges like low-contrast boundaries, small tumor regions, and overlapping tumors. Furthermore, the lightweight design of the model ensures its suitability for real-time clinical applications, making it a robust tool for automated tumor segmentation. This study underscores the potential of spatial attention mechanisms to significantly enhance medical imaging models and paves the way for more effective diagnostic tools. Full article
(This article belongs to the Special Issue Medical Artificial Intelligence and Data Analysis)
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17 pages, 1183 KiB  
Article
GAN-Based Novel Approach for Generating Synthetic Medical Tabular Data
by Rashid Nasimov, Nigorakhon Nasimova, Sanjar Mirzakhalilov, Gul Tokdemir, Mohammad Rizwan, Akmalbek Abdusalomov and Young-Im Cho
Bioengineering 2024, 11(12), 1288; https://doi.org/10.3390/bioengineering11121288 - 18 Dec 2024
Cited by 1 | Viewed by 1350
Abstract
The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information [...] Read more.
The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving “Good” similarity and “Excellent” utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond. Full article
(This article belongs to the Special Issue Medical Artificial Intelligence and Data Analysis)
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