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Artificial Intelligence for Precision Analysis and Decision Making in Medical...
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Artificial Intelligence for Precision Analysis and Decision Making in Medical Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 6249

Special Issue Editor

Dr. Jia Wu

E-Mail Website
Guest Editor
1. Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
2. School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: medical informatics; big data research; wireless network; decision-making system; machine learning; knowledge management; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Medical imaging is an important basis for clinical analysis and efficacy judgment. In developing-region hospitals, due to differences in subjective judgments and a lack of experienced physicians, making accurate judgments based on medical images is remarkably difficult. Despite tremendous efforts from academics and industry, there is still a need for universal solutions for rare diseases and improved diagnostic performance. In the last decade, with the development of machine learning and neural networks, it has become possible for computer technology to intelligently process large-scale and multimodal medical data and extract meaningful deep features. Therefore, our Special Issue aims to provide contributions sharing innovative ideas for the automatic analysis of medical images using artificial intelligence to aid the medical community.

Potential topics include, but are not limited to:

  • Disease detection solutions based on intelligent analysis of medical images (such as MRI, X-ray, CT, ultrasound images, etc.);
  • The semantic segmentation scheme for medical images;
  • The intelligent lesion detection scheme for medical images;
  • Accurate registration of multiparameter, cross-modal medical images;
  • Feature fusion of medical images and medical records;
  • Medical image generation;
  • Medical image classification;
  • Building a more robust medical image management platform.

Dr. Jia Wu
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 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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • medical images
  • artificial intelligence
  • image analysis
  • auxiliary diagnosis
  • neural network
  • healthcare informatics

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

Published Papers (3 papers)

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Research

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11 pages, 2637 KB  
Article
AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels
by Seyed Ehsan Seyed Bolouri, Masood Dehghan, Mahdiar Nekoui, Brian Buchanan, Jacob L. Jaremko, Dornoosh Zonoobi, Arun Nagdev and Jeevesh Kapur
Diagnostics 2025, 15(17), 2145; https://doi.org/10.3390/diagnostics15172145 - 25 Aug 2025
Viewed by 824
Abstract
Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods [...] Read more.
Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods: In this multi-reader, multi-case study, 14 clinicians of varying experience reviewed 374 retrospectively selected LUS scans (cine clips from the PLAPS point, obtained using three different probes) from 359 patients across six centers in the U.S. and Canada. In phase one, readers scored the likelihood (0–100) of pleural effusion and consolidation/atelectasis without AI. After a 4-week washout, they re-evaluated all scans with AI-generated bounding boxes. Performance metrics included area under the curve (AUC), sensitivity, specificity, and Fleiss’ Kappa. Subgroup analyses examined effects by reader experience. Results: For pleural effusion, AUC improved from 0.917 to 0.960, sensitivity from 77.3% to 89.1%, and specificity from 91.7% to 92.9%. Fleiss’ Kappa increased from 0.612 to 0.774. For consolidation/atelectasis, AUC rose from 0.870 to 0.941, sensitivity from 70.7% to 89.2%, and specificity from 85.8% to 89.5%. Kappa improved from 0.427 to 0.756. Conclusions: AI assistance enhanced clinician detection of pleural effusion and consolidation/atelectasis in LUS scans, particularly benefiting less experienced users. Full article
(This article belongs to the Special Issue Artificial Intelligence for Precision Analysis and Decision Making in Medical Imaging)
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21 pages, 4991 KB  
Article
Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images
by Baolong Lv, Feng Liu, Yulin Li, Jianhua Nie, Fangfang Gou and Jia Wu
Diagnostics 2023, 13(6), 1063; https://doi.org/10.3390/diagnostics13061063 - 10 Mar 2023
Cited by 25 | Viewed by 3522
Abstract
Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, [...] Read more.
Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods. Full article
(This article belongs to the Special Issue Artificial Intelligence for Precision Analysis and Decision Making in Medical Imaging)
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Review

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21 pages, 1247 KB  
Review
Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis
by Rahul Kumar, Kiran Marla, Puja Ravi, Kyle Sporn, Rohit Srinivas, Swapna Vaja, Alex Ngo and Alireza Tavakkoli
Diagnostics 2025, 15(18), 2295; https://doi.org/10.3390/diagnostics15182295 - 10 Sep 2025
Viewed by 464
Abstract
Joint degeneration is a major global health issue requiring improved diagnostic and prognostic tools. This review examines whether integrating Bayesian graphical models with multiscale medical imaging can enhance detection, analysis, and prediction of joint degeneration compared to traditional single-scale methods. Recent advances in [...] Read more.
Joint degeneration is a major global health issue requiring improved diagnostic and prognostic tools. This review examines whether integrating Bayesian graphical models with multiscale medical imaging can enhance detection, analysis, and prediction of joint degeneration compared to traditional single-scale methods. Recent advances in quantitative MRI, such as T2 mapping, enable early detection of subtle cartilage changes, supporting earlier intervention. Bayesian graphical models provide a flexible framework for representing complex relationships and updating predictions as new evidence emerges. Unlike prior reviews that address Bayesian methods or musculoskeletal imaging separately, this work synthesizes these domains into a unified framework that spans molecular, cellular, tissue, and organ-level analyses, providing methodological guidance and clinical translation pathways. Key topics within Bayesian inference include multiscale analysis, probabilistic graphical models, spatial-temporal modeling, network connectivity analysis, advanced imaging biomarkers, quantitative analysis, quantitative MRI techniques, radiomics and texture analysis, multimodal integration strategies, uncertainty quantification, variational inference approaches, Monte Carlo methods, and model selection and validation, as well as diffusion models for medical imaging and Bayesian joint diffusion models. Additional attention is given to diffusion models for advanced medical image generation, addressing challenges such as limited datasets and patient privacy. Clinical translation and validation requirements are emphasized, highlighting the need for rigorous evaluation to ensure that synthesized or processed images maintain diagnostic accuracy. Finally, this review discusses implementation challenges and outlines future research directions, emphasizing the potential for earlier diagnosis, improved risk assessment, and personalized treatment strategies to reduce the growing global burden of musculoskeletal disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence for Precision Analysis and Decision Making in Medical Imaging)
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