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Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 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: 30 September 2026 | Viewed by 6641

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Dr. Lawrence Chan

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Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
Interests: bioinformatics; imaging informatics; clinical decision support
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Special Issue Information

Dear Colleagues,

The use of Artificial Intelligence (AI) in Computer-Aided Detection (CAD) has led to significant advancements in biomedical applications. AI encompasses the development of intelligent machines that can simulate human intelligence, enabling them to learn from large datasets and make predictions or decisions based on complex patterns and algorithms. CAD systems, on the other hand, aid healthcare professionals in the identification and analysis of various medical conditions, utilizing computer algorithms to improve accuracy and efficiency.

This Special Issue explores the integration of AI techniques within CAD systems to revolutionize biomedical applications. It aims to present work from researchers and practitioners from multidisciplinary backgrounds and discuss the latest advancements, challenges, and future prospects in this rapidly growing field.

Topics of interest within this Special Issue include, but are not limited to, the development and evaluation of novel AI algorithms for CAD in biomedical imaging, the application of machine learning techniques to enhance detection and diagnosis accuracy, the utilization of deep learning architectures in CAD systems, the integration of AI technologies into medical decision making, the impact of AI on CAD-assisted diagnosis and treatment planning, and ethical considerations surrounding the use of AI in biomedical applications.

The papers contributed to this Special Issue will provide valuable insights into the potential of AI-powered CAD systems in biomedical domains, paving the way for improved detection, diagnosis, prognosis, and personalized treatment strategies. Researchers and practitioners across fields such as computer science, biomedical engineering, radiology, medical imaging, and bioinformatics are encouraged to submit their original research, reviews, and case studies.

Dr. Lawrence Chan
Guest Editor

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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

artificial intelligence
computer-aided detection
CAD
biomedical applications
biomedical engineering
radiology
medical imaging
bioinformatics
deep learning
machine learning
disease diagnosis

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Artificial Intelligence for Computer-Aided Detection in Biomedical Applications in Bioengineering (18 articles)

Published Papers (8 papers)

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Editorial

Jump to: Research, Review

4 pages, 146 KB

Open AccessEditorial

Artificial Intelligence for Computer-Aided Detection in Biomedical Applications

by Lawrence Wing Chi Chan

Bioengineering 2026, 13(4), 448; https://doi.org/10.3390/bioengineering13040448 - 11 Apr 2026

Viewed by 609

Abstract

Artificial intelligence (AI) plays an important role in bioengineering that has promoted a paradigm shift in how disease diagnosis, treatment planning, and patient monitoring are performed [...] Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

Research

Jump to: Editorial, Review

20 pages, 11097 KB

Open AccessArticle

Explainable Quality Assessment and Measurement from Real-World Hip Ultrasound Cine Sweeps

by Adam McArthur, Stephanie Wichuk, Stephen Burnside, George Reed, Sukhdeep Dulai, Abhilash Hareendranathan and Jacob L. Jaremko

Bioengineering 2026, 13(6), 667; https://doi.org/10.3390/bioengineering13060667 - 8 Jun 2026

Viewed by 344

Abstract

This study evaluates Retuve, an open-source explainable pipeline for the automated analysis of infant hip ultrasound cine sweeps. Retuve combines segmentation, Graf-plane calibration, and frame filtering. In a retrospective multicenter study, we tested the full pipeline on an external set of 109 hips from a Canadian community clinic, with internal developmental validation of segmentation on 90 hips and Graf-plane calibration on 419 hips. On the external test set, Retuve achieved 100% specificity and 91% sensitivity for expert agreement regarding whether a sweep contained an analyzable frame, compared with 75% specificity and 96% sensitivity for a radiology fellow; specificity was based on 16 expert-negative examinations. For alpha angle and acetabular coverage, Retuve achieved consistency intraclass correlation coefficients (ICCs) of 0.77 and 0.74, comparable to the fellow’s 0.70 and 0.74. However, alpha-angle absolute agreement was lower (ICC 0.55, 95% confidence interval (CI) −0.07–0.81), consistent with systematic measurement bias. Internal developmental validation showed Component 1 mask mean average precision at 50% intersection-over-union (mAP50) of 0.753 and box mAP50 of 0.883 and a Component 2 ICC of 0.792. Retuve can select analyzable frames and recover measurements from variable-quality cine sweeps, but alpha-angle calibration requires refinement. Future prospective work should evaluate developmental dysplasia of the hip (DDH) diagnostic accuracy, clinical treatment decision support, and screening outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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24 pages, 12964 KB

Open AccessArticle

3DAD: Super-Resolution Image Synthesis from Anisotropic CT Images Using a Three-Dimensional Adversarial Diffusion Model

by Jianliang Lu, Ho Ming Cheng, Benjamin Xin Hao Fang, Chun On Anderson Tsang, Sarah Yu, Wai-Kay Seto, Philip Leung Ho Yu and Keith Wan-Hang Chiu

Bioengineering 2026, 13(6), 595; https://doi.org/10.3390/bioengineering13060595 - 22 May 2026

Viewed by 311

Abstract

High-resolution thin-slice computed tomography (CT) images are often compressed into lower-quality thick-slice images for long-term storage, necessitating synthesis for medical diagnosis. In this paper, we propose a novel 3D adversarial diffusion model (3DAD) for high-fidelity synthesis of thin-slice CT from compressed thick-slice CT. 3DAD is composed of a generator and a discriminator for synthesizing denoised thin-slice images from random noise and source images and distinguishing between noised samples from real and denoised synthetic thin-slice images. Specific models were trained on two-slice to six-slice scenarios for abdominal data, using thick-slice CT compressed from real thin-slice CT as the source. 3DAD was evaluated at the time of HCC diagnosis, at the observation and patient levels, using real thin-slice and synthetic thin-slice CT, with DeLong’s test to compare the similarity of receiver operating characteristic (ROC) curves. We further evaluated 3DAD on real-world data with both thin and thick images, with the synthetic image quality assessed by radiologists and in radiomics feature analysis. Based on the external dataset with 548 samples, the achieved mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) values were 81.374, 29.478, and 0.916, respectively, for the five-slice scenarios at the portal venous phase. The Areas Under Curves (AUCs) achieved were 0.896 on synthetic thin-slice images compared with 0.889 on real thin-slice images at the observation level (p = 0.028) and 0.854 versus 0.846, correspondingly, at the patient level (p = 0.055). For evaluation on the real-world testing dataset after fine-tuning at the portal venous phase, the MSE, PSNR, and SSIM were 70.435, 30.243, and 0.94, respectively. Radiologist evaluation confirmed the high quality of the synthetic image, with no significant difference in the majority of cases across all five parameters, except for radiologist 2, in realistic and consistent situations, under which at least 41 of 43 synthetic images were assessed as equal to or above grade 3. Our 3DAD enabled the synthesis of thick-slice CT images into high-resolution thin-slice images, facilitating high-fidelity volume image application in HCC diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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25 pages, 3884 KB

Open AccessArticle

Deep-Learning-Based 3D Dose Distribution Prediction for VMAT Lung Cancer Treatment Using an Enhanced UNet3D Architecture with Composite Loss Functions

by Philip Chung Yin Mak, Luoyi Kong and Lawrence Wing Chi Chan

Bioengineering 2026, 13(5), 490; https://doi.org/10.3390/bioengineering13050490 - 23 Apr 2026

Viewed by 1154

Abstract

The high complexity of radiation therapy for lung cancer necessitates effective planning of advanced treatments such as Volumetric Modulated Arc Therapy (VMAT) by radiation oncologists. The current VMAT treatment planning process typically involves extensive manual interaction and a time-consuming, trial-and-error, iterative approach that requires planners’ experience. This can lead to varying levels of plan quality. To improve the quality of radiotherapy treatment plans quickly and accurately, this research presents a new architecture, Enhanced UNet3D, to generate three-dimensional (3-D) dose distributions for lung cancer patients. Enhanced UNet3D utilises a symmetric encoder–decoder architecture with residual connections and a target region-attention module to achieve high accuracy in dose shaping within the PTV. A new composite objective function, Enhanced Combined Loss (ECLoss), that includes both SharpLoss, a structure-aware DVH-guided loss, and 3D gradient regularisation, has been developed to address voxel-level class imbalance and achieve realistic spatial dose falloff. This research utilised a retrospective dataset of 170 VMAT plans to train and validate the proposed model. On the test set (n = 14), the model demonstrated exceptional overall accuracy, with a Mean Absolute Error (MAE) of 0.238 ± 0.075 Gy and a structural similarity index measure (SSIM) of 0.970 ± 0.005. Moreover, the model can perform near-real-time inference at approximately 0.5 s per patient, representing a significant reduction in computational resources compared to other architectures. Therefore, these results demonstrate that the Enhanced UNet3D model with ECLoss is a clinically feasible tool for the rapid evaluation and quality assurance of radiotherapy treatment plans and may reduce the need for manual trial-and-error in VMAT workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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20 pages, 4643 KB

Open AccessArticle

Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings

by Anchal Kumari, Punam Rattan, Anand Kumar Shukla, Sita Rani, Aman Kataria, Hong Min and Taeho Kim

Bioengineering 2026, 13(4), 456; https://doi.org/10.3390/bioengineering13040456 - 13 Apr 2026

Cited by 1 | Viewed by 900

Abstract

Skin cancer is caused by aberrant cells that proliferate uncontrollably after unrepaired DNA damage results in mutations in the epidermis. The majority of skin cancer is caused by high UV exposure from the sun, tanning beds, or sunlamps. Due to sociocultural hurdles, limited access to specialized dermatological care, and low public knowledge, many nations, including India, have higher mortality rates and late-stage presentations. The unequal distribution of specialized dermatological treatments, particularly in rural and underdeveloped areas, makes detection and treatment more difficult. For skin cancer, one of the most prevalent malignancies with a high death rate, early detection is crucial. This study gathered 1200 dermoscopic images from two clinics in Himachal Pradesh in order to solve these problems. In order to automatically classify dermoscopic clinical images into melanoma and non-melanoma skin cancer categories, this study compares VGG16 with ResNet-50. Preprocessing, lesion segmentation, and classification are all part of the suggested approach. A collection of 1200 dermoscopic images with clinical annotations was used to improve the models. ResNet-50 outperformed VGG16 in tests, with 93% accuracy and 96% AUC-ROC as opposed to 89% and 94%, respectively. These results emphasize how crucial model selection and preprocessing are to diagnostic performance. Ensemble methods, multi-class classification, explainability integration, and clinical validation will be investigated in order to facilitate the implementation of AI-assisted dermatological diagnostic tools. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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28 pages, 54910 KB

Open AccessArticle

Interpretable Machine Learning-Based Concentric Regional Analysis of OCTA Images for Enhanced Diabetic Retinopathy Detection

by Shrouk Mohamed Osman, Ahmed Alksas, Hossam Magdy Balaha, Ali Mahmoud, Ahmed Gamal, Mohamed El-Said Abdel-Hady, Mohamed Moawad Abdelsalam, Abeer Twakol Khalil, Ashraf Sewelam and Ayman El-Baz

Bioengineering 2026, 13(4), 450; https://doi.org/10.3390/bioengineering13040450 - 12 Apr 2026

Viewed by 546

Abstract

Diabetic retinopathy (DR) remains a major cause of vision loss in patients with diabetes, and earlier recognition of retinal vascular abnormalities may improve risk stratification and clinical follow-up. Optical coherence tomography angiography (OCTA) provides a noninvasive way to visualize the retinal microvasculature and may detect DR-related changes before they are evident on routine clinical assessment. In this work, we investigated whether dividing OCTA images into anatomically defined retinal regions could improve DR classification and clarify which regions carry the greatest discriminative information. The study included 188 OCTA images: 67 from normal eyes, 57 from eyes with mild DR, and 64 from eyes with moderate DR. Each image was divided into seven concentric regions centered on the fovea, and vessel-density features were extracted from each region. Ten machine learning classifiers were trained and compared at the regional level. For each region, the best-performing classifier was retained, and the final prediction was obtained with a majority-voting ensemble. To examine model behavior, Local Interpretable Model-Agnostic Explanations (LIME) were applied. Performance was also compared with that of a transfer-learning MobileNet model trained on whole OCTA images. On the held-out patient-level test set, the ensemble model achieved 97% accuracy, 98% precision, 97% recall, and a 97% F1-score for three-class classification. These results were higher than those obtained with the tested whole-image transfer-learning baselines. The interpretability analysis consistently identified the parafoveal regions as the most informative for classification. Among the seven regions, Region 3 showed the highest overall contribution, followed by Regions 2 and 5, whereas Region 5 became more influential in moderate DR. These results suggest that regional analysis of OCTA-derived vessel density can improve both classification performance and interpretability in DR assessment. The findings also indicate that parafoveal vascular alterations carry substantial discriminative value in distinguishing normal, mild DR, and moderate DR cases. Validation in larger, independent cohorts from multiple centers will be necessary to confirm the generalizability of these findings. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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21 pages, 1612 KB

Open AccessArticle

Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases

by Feiyan Jiao, Yiming Liu, Zhongshun Tang, Shuai Han, Tian Li, Yuanpeng Zhang, Peihua Liu, Guodong Huang, Hao Li, Yongping Zheng, Zhou Li and Sai-Kit Lam

Bioengineering 2026, 13(2), 137; https://doi.org/10.3390/bioengineering13020137 - 24 Jan 2026

Viewed by 1124

Abstract

This study aims to investigate the predictive value of pre-treatment multi-phasic contrast-enhanced computed tomography (CECT) radiomic features for treatment resistance in patients with rat sarcoma virus (RAS)-mutated colorectal liver metastases (CRLMs) receiving bevacizumab-based chemotherapy. Seventy-three samples with RAS-mutated CRLMs receiving bevacizumab-combined chemotherapy regimens were evaluated. Radiomic features were extracted from arterial phase (AP), portal venous phase (PVP), AP-PVP subtraction image, and Delta phase (DeltaP, calculated as AP-to-PVP ratio) images. Three groups of radiomics features were extracted for each phase, including peritumor, core tumor, and whole-tumor regions. For each of the four phases, a two-sided independent Mann–Whitney U test with the Bonferroni correction and K-means clustering was applied to the remnant features for each phase. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied for further feature selection. Six machine learning algorithms were then used for model development and validated on the independent testing cohort. Results showed peritumoral radiomic features and features derived from Laplacian of Gaussian (LoG) filtered images were dominant in all the compared machine learning algorithms; NB models yielded the best-performing prediction (Avg. training AUC: 0.731, Avg. testing AUC: 0.717) when combining all features from different phases of CECT images. This study demonstrates that peritumoral radiomic features and LoG-filtered pre-treatment multi-phasic CECT images were more predictive of treatment response to bevacizumab-based chemotherapy in RAS-mutated CRLMs compared to core tumor features. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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Review

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21 pages, 978 KB

Open AccessReview

Artificial Intelligence for Computer-Aided Detection in Endovascular Interventions: Clinical Applications, Validation, and Translational Perspectives

by Rasit Dinc and Nurittin Ardic

Bioengineering 2026, 13(4), 399; https://doi.org/10.3390/bioengineering13040399 - 29 Mar 2026

Viewed by 941

Abstract

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications, 2nd Edition)

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