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AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 17730

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Editors

Dr. Mohammed A. Al-masni

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Guest Editor

Department of Artificial Intelligence and Data Science, College of Artificial Intelligence Convergence, Sejong University, Seoul, Republic of Korea
Interests: medical image analysis; artificial intelligence; deep learning; abnormalities segmentation and diagnosis; biomedical image/signal processing; image synthesis; MRI motion artifacts correction
Special Issues, Collections and Topics in MDPI journals

Dr. Kanghyun Ryu

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Guest Editor

Intelligence and Interaction Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea
Interests: medical image reconstruction; medical image synthesis; image segmentation; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has recently revolutionized healthcare with advancements in medical imaging and sensing technologies. These advancements have resulted in automated, precise, and efficient diagnosis and prognosis tools, significantly improving disease detection and patient care. AI algorithms demonstrate exceptional proficiency in analyzing medical images (MRI, CT, PET, etc.) and signals (EEG, ECG, EMG) for the classification of abnormalities, as well as the detection and segmentation of suspicious regions. This improves diagnostic accuracy, expedites decision-making processes, and offers benefits across various medical specialties. Moreover, researchers are actively addressing challenges such as artifact correction, image synthesis, and multi-modality registration to enhance medical data analysis, leading to more reliable clinical decisions and treatment plans. The integration of AI with medical imaging and sensing presents vast potential. It enables early disease detection, personalized treatment plans, and enhanced monitoring of various conditions. With ongoing advancements in AI and computing capabilities, there is potential for further innovation in healthcare, enabling more precise, efficient, and patient-centric healthcare delivery.

The second edition of this Special Issue invites novel research and technical advancements in biomedical imaging and sensing technologies. Original research papers and comprehensive reviews focusing on cutting-edge methodologies are encouraged.

Topics of interest for this Special Issue include, but are not limited to, the following:

Biomedical imaging;
Biosignals;
Medical image analysis;
Abnormalities classification and detection;
Medical image segmentation;
Medical image reconstruction;
Medical image denoising;
Medical image registration;
AI in biomedical systems;
Computer-aided diagnosis systems.

Dr. Mohammed A. Al-masni
Dr. Kanghyun Ryu
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 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

biomedical imaging
biosignals
medical image analysis
abnormalities classification and detection
medical image segmentation
medical image reconstruction
medical image denoising
medical image registration
AI in biomedical systems
computer-aided diagnosis systems

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Related Special Issue

AI Advancements in Healthcare: Medical Imaging and Sensing Technologies in Bioengineering (6 articles)

Published Papers (11 papers)

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Research

Jump to: Review, Other

19 pages, 4704 KB

Open AccessArticle

Development of an Integrated Radiotherapy Simulation Platform with AI-Driven Segmentation and Ray-Casting-Based Dosimetric Evaluation

by Cheng-Yen Lee, Hsiao-Ju Fu, Pin-Yi Chiang, Hien Vu-Dinh, Hung-Ching Chang and Hong-Tzong Yau

Bioengineering 2026, 13(5), 572; https://doi.org/10.3390/bioengineering13050572 - 18 May 2026

Viewed by 466

Abstract

Radiotherapy simulation is essential for accurately targeting tumors while preserving healthy tissue, ensuring treatment precision and safety. This study aimed to develop an integrated radiotherapy simulation system capable of automated segmentation, dose estimation, and collision detection within a virtual planning environment to enhance efficiency and reduce costs in radiotherapy treatment planning. The Point Transformer model was applied to organ point cloud data derived from CT medical imaging for automated segmentation. Farthest point sampling (FPS) was employed to downsample the data before training. To enhance the accuracy and anatomical fidelity of the AI-generated segmentation results, reconstruction and refinement algorithms, including k-d tree, outlier removal, marching cubes, and surface smoothing, were implemented. Beam penetration simulation with the ray casting algorithm was employed for correction-based dose estimation. A collision detection module was incorporated to identify potential machine–machine or machine–patient interactions. The entire workflow was executed within a Unity 3D-based virtual simulation environment. As a result, the Point Transformer model demonstrated high segmentation accuracy, achieving Dice scores of 93.86

\pm

1.50% for single-organ and 91.86

\pm

3.25% for multi-organ cases, surpassing the performance of PointNet++. Applying ray casting for the refined surface meshes generated through post-processing enabled accurate dose estimation with discrepancies of 3.5% (brain), 5.9% (liver), and 13.8% (lung) compared to a Pinnacle TPS. The proposed method provides a low-cost and adaptable solution that enables easy modification and further development, making it particularly suitable for widespread applications in radiotherapy research, education, and clinical workflow optimization. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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22 pages, 2559 KB

Open AccessArticle

SEG-FAUSP: Anatomical Structure Segmentation of the Standard Sections of Fetal Abdominal Ultrasounds

by Jianhui Chen, Peizhong Liu, Xiaying Yang, Xiaoling Wang, Xiuming Wu, Zhonghua Liu and Shunlan Liu

Bioengineering 2026, 13(4), 403; https://doi.org/10.3390/bioengineering13040403 - 31 Mar 2026

Viewed by 928

Abstract

This study addresses the challenge of the difficult identification of organ structures in the standard sections of fetal abdominal ultrasounds. A deep learning-based multi-task model named SEG-FAUSP was developed to segment the core anatomical structures of seven key fetal abdominal ultrasound sections. We collected fetal abdominal ultrasound images from pregnant women in the mid-pregnancy period (18–24 weeks) using various mainstream ultrasound devices, and professional physicians annotated key anatomical structures (e.g., umbilical veins, gastric bubbles, spine) in the images. Based on an improved deep learning framework, the model accurately segments and locates the target organ structures through a parallel dual-branch semantic segmentation network, which avoids the over-reliance on large-scale pre-trained data in traditional methods. Experimental results show that the model achieves excellent performance in anatomical structure segmentation, with the intersection over union of the bladder and gastric bubble both reaching above 0.84; its segmentation accuracy for complex structures such as the inferior vena cava is also significantly superior to the baseline model. As an end-to-end model, it simplifies the clinical interpretation process of fetal abdominal ultrasound, reduces the risk of missed diagnoses caused by unclear organ identification, provides an efficient auxiliary tool for prenatal screening in grassroots medical institutions, and is of great significance for improving the quality of newborns. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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37 pages, 5460 KB

Open AccessArticle

From Infancy to Aging: Precise Brain Age Estimation via Hybrid CoTResNet3D and CrossViT Models on T1-Weighted Imaging

by Xinyu Zhu, Shen Sun, Hongjian Gao, Yutong Wu, Zhenrong Fu and Lan Lin

Bioengineering 2026, 13(3), 315; https://doi.org/10.3390/bioengineering13030315 - 9 Mar 2026

Cited by 1 | Viewed by 1172

Abstract

Accurate estimation of brain age from structural magnetic resonance imaging (MRI) serves as a vital biomarker for quantifying individual neurobiological aging and identifying risks for neurological disorders. However, developing robust models that generalize across the entire lifespan (from infancy to aging) remains challenging due to heterogeneous maturation/degeneration patterns, limited cross-center generalizability, and insufficient temporal reliability evaluation. To address these limitations, we curated a large-scale, multi-center T1-weighted MRI dataset across 27 public cohorts. Of these, 22,271 scans from 17 cohorts (aged 0–96 years) formed the primary foundation for model development, complemented by 10 additional cohorts utilized for independent multi-center evaluation and robustness testing. We propose ResNet-CrossViT, a novel hybrid architecture that synergistically combines a 3D Contextual Transformer-ResNet (CoTResNet3D) backbone for enriched local feature extraction and a CrossVision Transformer (CrossViT) module for cross-scale global dependency modeling. The model was rigorously evaluated on an internal test set, an unseen external dataset for cross-center validation, a longitudinal dataset for assessing temporal consistency, and a test–retest dataset for measuring reproducibility. On the internal test set, ResNet-CrossViT achieved a mean absolute error (MAE) of 2.72 years and a maximal MAE (mMAE) of 5.10 years, demonstrating marked performance improvements, particularly within the challenging adolescent cohort. The model maintained strong generalization on the unseen dataset (MAE = 4.19 years) and exhibited superior longitudinal consistency (Mean Absolute Difference Error, MAdE = 3.68) and excellent test–retest reliability (Intraclass Correlation Coefficient, ICC = 0.994). By integrating a large-scale, heterogeneous lifespan dataset with a hybrid architecture that effectively captures both local structural details and global long-range interactions, our study provides a precise, generalizable, and reliable framework for brain age estimation. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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20 pages, 4571 KB

Open AccessArticle

Artificial Intelligence-Based Automated Assessment of the Four-Chamber View in Fetal Cardiac Ultrasound Videos

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

Bioengineering 2026, 13(3), 303; https://doi.org/10.3390/bioengineering13030303 - 5 Mar 2026

Viewed by 1335

Abstract

The clinical application of artificial intelligence (AI) can provide technical support for examiners and improve obstetric workflow efficiency. In this study, we developed AI models that automatically extract the four-chamber view (4CV) from fetal cardiac ultrasound videos and compute the cardiothoracic area ratio, cardiac axis, and cardiac position for prenatal screening of congenital heart disease. Fetal cardiac ultrasound videos from 301 patients in the second trimester were analyzed. The 4CV was automatically extracted using YOLOv7, followed by image segmentation with UNet 3+ and SegFormer, after which automated parameter calculation and estimation were performed. A clinical comparison study involving 22 obstetricians was conducted to evaluate the screening performance of the AI models. The models demonstrated stable performance in both normal and abnormal cases, including examinations acquired using different ultrasound systems. Furthermore, the AI models achieved screening performance comparable to that of expert obstetricians. These findings indicate that the proposed AI framework enables reliable 4CV extraction and accurate biometric parameter computation. This fully automated approach has the potential to reduce missed abnormalities and improve the consistency of fetal cardiac ultrasound screening. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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12 pages, 1251 KB

Open AccessArticle

Inception U-Net for Enhanced Breast Ultrasound Image Segmentation Using Transfer Learning

by Yeonhyo Choi, Myoung Nam Kim and Sungdae Na

Bioengineering 2026, 13(2), 181; https://doi.org/10.3390/bioengineering13020181 - 4 Feb 2026

Cited by 2 | Viewed by 1256

Abstract

Background: Breast cancer diagnosis increasingly relies on ultrasound imaging, but challenges related to operator dependency and image quality limitations necessitate automated segmentation approaches. Traditional U-Net architectures, while widely used for medical image segmentation, suffer from shallow encoder structures that limit feature extraction capabilities. Methods: This study proposes an enhanced segmentation model that replaces the conventional U-Net encoder with an Inception architecture and employs transfer learning using ImageNet pre-trained weights. The model was trained and evaluated on a dataset of 900 breast ultrasound images from Kyungpook National University Hospital. Performance evaluation utilized multiple metrics including Intersection over Union (IoU), Dice coefficient, precision, and recall scores. Results: The proposed Inception U-Net achieved superior performance with an IoU score of 0.7774, Dice score of 0.8491, precision score of 0.7081, and recall score of 0.7174, demonstrating approximately 5% improvement over baseline U-Net architecture across all evaluation metrics. Conclusions: The integration of Inception modules within the U-Net architecture effectively addresses feature extraction limitations in breast ultrasound segmentation. Transfer learning from ImageNet datasets proves beneficial even across domain differences, establishing a foundation for broader medical imaging applications. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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14 pages, 1368 KB

Open AccessArticle

Three-Dimensional Visualization and Detection of the Pulmonary Venous–Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening

by Reina Komatsu, Masaaki Komatsu, Katsuji Takeda, Naoaki Harada, Naoki Teraya, Shohei Wakisaka, Takashi Natsume, Tomonori Taniguchi, Rina Aoyama, Mayumi Kaneko, Kazuki Iwamoto, Ryu Matsuoka, Akihiko Sekizawa and Ryuji Hamamoto

Bioengineering 2026, 13(1), 100; https://doi.org/10.3390/bioengineering13010100 - 15 Jan 2026

Cited by 2 | Viewed by 1116 | Correction

Abstract

Total anomalous pulmonary venous connection (TAPVC) is one of the most severe congenital heart defects; however, prenatal diagnosis remains suboptimal. A normal fetal heart has a junction between the pulmonary venous (PV) and left atrium (LA). In contrast, no junctions are observed in patients with TAPVC. In the present study, we attempted to visualize and detect fetal PV-LA connections using artificial intelligence (AI) trained on the fetal cardiac ultrasound videos of 100 normal cases and six TAPVC cases. The PV-LA aggregate area was segmented using the following three-dimensional (3D) segmentation models: SegResNet, Swin UNETR, MedNeXt, and SegFormer3D. The Dice coefficient and 95% Hausdorff distance were used to evaluate segmentation performance. The mean values of the shortest PV-LA distance (PLD) and major axis angle (PLA) in each video were calculated. These methods demonstrated sufficient performance in visualizing and detecting the PV-LA connection. In terms of TAPVC screening performance, MedNeXt-PLD and SegResNet-PLA achieved mean area under the receiver operating characteristic curve values of 0.844 and 0.840, respectively. Overall, this study shows that our approach can support unskilled examiners in capturing the PV-LA connection and has the potential to improve the prenatal detection rate of TAPVC. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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25 pages, 4182 KB

Open AccessArticle

New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis

by Nagwan Abdel Samee, Mohammed A. Al-masni, Eman N. Marzban, Abobakr Khalil Al-Shamiri, Mugahed A. Al-antari, Maali Ibrahim Alabdulhafith, Noha F. Mahmoud and Yasser M. Kadah

Bioengineering 2025, 12(10), 1130; https://doi.org/10.3390/bioengineering12101130 - 21 Oct 2025

Cited by 1 | Viewed by 1650

Abstract

Gait analysis is essential in the evaluation of neuromuscular and musculoskeletal disorders; however, traditional approaches based on expert visual observation remain subjective and often lack consistency. Accurate and objective assessment of gait impairments is critical for early diagnosis, monitoring rehabilitation progress, and guiding clinical decision-making. Although Gait Energy Images (GEI) have become widely used in automated, vision-based gait analysis, they are limited in capturing boundary details and time-resolved motion dynamics, both critical for robust clinical interpretation. To overcome these limitations, we introduce four novel gait representation maps: the time-coded gait boundary image (tGBI), color-coded GEI (cGEI), time-coded gait delta image (tGDI), and color-coded boundary-to-image transform (cBIT). These representations are specifically designed to embed spatial, temporal, and boundary-specific features of the gait cycle, and are constructed from binary silhouette sequences through straightforward yet effective transformations that preserve key structural and dynamic information. Experiments on the INIT GAIT dataset demonstrate that the proposed representations consistently outperform the conventional GEI across multiple machine learning models and classification tasks involving different numbers of gait impairment categories (four and six classes). These findings highlight the potential of the proposed approaches to enhance the accuracy and reliability of automated clinical gait analysis. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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Review

Jump to: Research, Other

36 pages, 1533 KB

Open AccessReview

Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches

by Kadir Sabanci, Busra Aslan and Muhammet Fatih Aslan

Bioengineering 2026, 13(5), 555; https://doi.org/10.3390/bioengineering13050555 - 15 May 2026

Viewed by 1075

Abstract

Recent advances in medical image segmentation have introduced a wide spectrum of deep learning paradigms, including 2D/3D convolutional neural networks (CNNs), transformer-based architectures, vision-language models (VLMs), prompt-driven foundation models such as Segment Anything Model (SAM), and diffusion-based approaches. Although these methods have demonstrated remarkable performance across MRI, CT, PET, ultrasound, and endoscopic imaging, the rapid proliferation of architectures has created methodological uncertainty regarding optimal model selection under varying clinical and data constraints. Existing surveys primarily focus on architectural categorization, yet provide limited guidance for decision-oriented model selection. This study presents a comprehensive and decision-guided survey that systematically analyzes segmentation paradigms across imaging modalities, task types, dataset characteristics, and evaluation protocols. Beyond taxonomy, we propose a practical model selection framework that links clinical scenarios, such as small lesion detection, multi-organ 3D segmentation, limited-data regimes, and domain shift, to appropriate segmentation strategies. Furthermore, robustness, generalization, annotation variability, and benchmarking reproducibility are critically examined. By integrating architectural taxonomy, cross-modal comparative analysis, and a structured decision framework, this work provides a clinically oriented roadmap for selecting segmentation methods and highlights future research directions toward reliable and reproducible medical AI systems. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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32 pages, 1030 KB

Open AccessReview

A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation

by Dina Koishiyeva, Dinargul Mukhammejanova, Jeong Won Kang and Assel Mukasheva

Bioengineering 2025, 12(12), 1312; https://doi.org/10.3390/bioengineering12121312 - 29 Nov 2025

Cited by 3 | Viewed by 4520

Abstract

Medical image segmentation has undergone significant changes in recent years, mainly due to the development of base models. The introduction of the Segment Anything Model (SAM) represents a major shift from task-specific architectures to universal architectures. This review discusses the adaptation of SAM in medical visualisation, focusing on three primary domains. Firstly, multimodal fusion frameworks implement semantic alignment of heterogeneous visual methods. Secondly, volumetric extensions transition from slice-based processing to native 3D spatial reasoning with architectures such as SAM3D, ProtoSAM-3D, and VISTA3D. Thirdly, uncertainty-aware architectures integrate probabilistic calibration for clinical interpretability, as illustrated by the SAM-U and E-Bayes SAM models. A comparative analysis reveals that SAM derivatives with effective parameters achieve Dice coefficients of 81–95%, while concomitantly reducing annotation requirements by 56–73%. Future research directions include incorporating adaptive domain hints, Bayesian self-correction mechanisms, and unified volumetric frameworks to enable autonomous generalisation across diverse medical imaging contexts. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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24 pages, 786 KB

Open AccessReview

Deep Learning for CT Synthesis in Radiotherapy

by Yike Guo, Yi Luo, Hamed Hooshangnejad, Rui Zhang, Xue Feng, Quan Chen, Wilfred Ngwa and Kai Ding

Bioengineering 2025, 12(12), 1297; https://doi.org/10.3390/bioengineering12121297 - 25 Nov 2025

Cited by 1 | Viewed by 2697

Abstract

With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy (IGART) to the simulation-free workflow. This review provides a comprehensive overview of recent studies on DL-based sCT synthesis in radiotherapy from multiple imaging modalities, including Cone-Beam CT (CBCT), Magnetic Resonance Imaging (MRI), and diagnostic CT, and discusses their clinical applications in CBCT-based online adaptive radiotherapy, MRI-guided radiotherapy, and simulation-free workflows. We also examine the architectures of representative DL models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) and summarize emerging training strategies. Finally, we discuss current challenges of clinical translation of DL algorithms into clinical practice and suggest potential directions for future research. Overall, this paper highlights the potential of AI-driven sCT generation to advance treatment planning by reducing imaging burden, improving dose accuracy, and accelerating workflow efficiency, thus ultimately improving the treatment outcome of patient care. Full article

(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition)

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