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Artificial Intelligence and Machine Learning in Clinical Practice: Advancing Medical Imaging Analysis

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 8144

Special Issue Editor

Dr. Guillaume Herpe

E-Mail Website
Guest Editor
1. Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
2. Labcom I3M, University of Poitiers, 86000 Poitiers, France
3. DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
Interests: artificial intelligence; emergency radiology; medical imaging

Special Issue Information

Dear Colleagues,

The use of AI in healthcare, especially imaging, is now widespread. In the U.S., 20% of mammograms use AI, and over 50% of emergency imaging centers deploy AI for fracture detection. Despite its rapid adoption, there is limited research on AI's transformative impact in healthcare. This Special Issue aims to spotlight studies focused on the clinical impacts of AI solutions, examining how they reshape diagnostics, streamline workflows, and enhance patient outcomes. By exploring both benefits and challenges, this Special Issue seeks to bridge the gap between technical advancement and clinical evidence, promoting a deeper understanding of AI’s evolving role in medical practice.

Dr. Guillaume Herpe
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine 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

  • artificial intelligence
  • effectiveness
  • transformative impact
  • patient-centered radiology
  • workflows

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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

12 pages, 1779 KB  
Article
Artificial Intelligence Algorithm Supporting the Diagnosis of Developmental Dysplasia of the Hip: Automated Ultrasound Image Segmentation
by Łukasz Pulik, Paweł Czech, Jadwiga Kaliszewska, Bartłomiej Mulewicz, Maciej Pykosz, Joanna Wiszniewska and Paweł Łęgosz
J. Clin. Med. 2025, 14(17), 6332; https://doi.org/10.3390/jcm14176332 - 8 Sep 2025
Viewed by 1046
Abstract
Background: Developmental dysplasia of the hip (DDH), if not treated, can lead to osteoarthritis and disability. Ultrasound (US) is a primary screening method for the detection of DDH, but its interpretation remains highly operator-dependent. We propose a supervised machine learning (ML) image [...] Read more.
Background: Developmental dysplasia of the hip (DDH), if not treated, can lead to osteoarthritis and disability. Ultrasound (US) is a primary screening method for the detection of DDH, but its interpretation remains highly operator-dependent. We propose a supervised machine learning (ML) image segmentation model for the automated recognition of anatomical structures in hip US images. Methods: We conducted a retrospective observational analysis based on a dataset of 10,767 hip US images from 311 patients. All images were annotated for eight key structures according to the Graf method and split into training (75.0%), validation (9.5%), and test (15.5%) sets. Model performance was assessed using the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). Results: The best-performing model was based on the SegNeXt architecture with an MSCAN_L backbone. The model achieved high segmentation accuracy (IoU; DSC) for chondro-osseous border (0.632; 0.774), femoral head (0.916; 0.956), labrum (0.625; 0.769), cartilaginous (0.672; 0.804), and bony roof (0.725; 0.841). The average Euclidean distance for point-based landmarks (bony rim and lower limb) was 4.8 and 4.5 pixels, respectively, and the baseline deflection angle was 1.7 degrees. Conclusions: This ML-based approach demonstrates promising accuracy and may enhance the reliability and accessibility of US-based DDH screening. Future applications could integrate real-time angle measurement and automated classification to support clinical decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice: Advancing Medical Imaging Analysis)
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13 pages, 371 KB  
Article
Real-Life Performance of a Commercially Available AI Tool for Post-Traumatic Intracranial Hemorrhage Detection on CT Scans: A Supportive Tool
by Léo Mabit, Maryne Lepoittevin, Martin Valls, Clément Thomas, Rémy Guillevin and Guillaume Herpe
J. Clin. Med. 2025, 14(13), 4403; https://doi.org/10.3390/jcm14134403 - 20 Jun 2025
Cited by 1 | Viewed by 3953
Abstract
Background: Traumatic brain injury (TBI) is a major cause of morbimortality in the world, and it can cause potential intracranial hemorrhage (ICH), a life-threatening condition that requires rapid diagnosis with computed tomography (CT). Artificial intelligence tools for ICH detection are now commercially [...] Read more.
Background: Traumatic brain injury (TBI) is a major cause of morbimortality in the world, and it can cause potential intracranial hemorrhage (ICH), a life-threatening condition that requires rapid diagnosis with computed tomography (CT). Artificial intelligence tools for ICH detection are now commercially available. Objectives: Investigate the real-world performance of qER.ai, an artificial intelligence-based CT hemorrhage detection tool, in a post-traumatic population. Methods: Retrospective monocentric observational study of a dataset of consecutively acquired head CT scans at the emergency radiology unit to explore brain trauma. AI performance was compared to ground truth determined by expert consensus. A subset of night shift cases with the radiological report of a junior resident was compared to the AI results and ground truth. Results: A total of 682 head CT scans were analyzed. AI demonstrated a sensitivity of 88.8% and a specificity of 92.1% overall, with a positive predictive value of 65.4% and a negative predictive value of 98%. AI’s performance was comparable to that of junior residents in detecting ICH, with the latter showing a sensitivity of 85.7% and a high specificity of 99.3%. Interestingly, the AI detected two out of three ICH cases missed by the junior residents. When AI assistance was integrated, the combined sensitivity improved to 95.2%, and the overall accuracy reached 98.8%. Conclusions: This study shows better performance from AI and radiologist residents working together than each one alone. These results are encouraging for rethinking the radiological workflow and the future of triage of this large population of brain traumatized patients in the emergency unit. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice: Advancing Medical Imaging Analysis)
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23 pages, 12145 KB  
Article
A Deep Learning-Based Detection and Segmentation System for Multimodal Ultrasound Images in the Evaluation of Superficial Lymph Node Metastases
by Roxana Rusu-Both, Marius-Cristian Socaci, Adrian-Ionuț Palagos, Corina Buzoianu, Camelia Avram, Honoriu Vălean and Romeo-Ioan Chira
J. Clin. Med. 2025, 14(6), 1828; https://doi.org/10.3390/jcm14061828 - 8 Mar 2025
Cited by 2 | Viewed by 2473
Abstract
Background/Objectives: Even with today’s advancements, cancer still represents a major cause of mortality worldwide. One important aspect of cancer progression that has a big impact on diagnosis, prognosis, and treatment plans is accurate lymph node metastasis evaluation. However, regardless of the imaging [...] Read more.
Background/Objectives: Even with today’s advancements, cancer still represents a major cause of mortality worldwide. One important aspect of cancer progression that has a big impact on diagnosis, prognosis, and treatment plans is accurate lymph node metastasis evaluation. However, regardless of the imaging method used, this process is challenging and time-consuming. This research aimed to develop and validate an automatic detection and segmentation system for superficial lymph node evaluation based on multimodal ultrasound images, such as traditional B-mode, Doppler, and elastography, using deep learning techniques. Methods: The suggested approach incorporated a Mask R-CNN architecture designed specifically for the detection and segmentation of lymph nodes. The pipeline first involved noise reduction preprocessing, after which morphological and textural feature segmentation and analysis were performed. Vascularity and stiffness parameters were further examined in Doppler and elastography pictures. Metrics, including accuracy, mean average precision (mAP), and dice coefficient, were used to assess the system’s performance during training and validation on a carefully selected dataset of annotated ultrasound pictures. Results: During testing, the Mask R-CNN model showed an accuracy of 92.56%, a COCO AP score of 60.7 and a validation score of 64. Furter on, to improve diagnostic capabilities, Doppler and elastography data were added. This allowed for improved performance across several types of ultrasound images and provided thorough insights into the morphology, vascularity, and stiffness of lymph nodes. Conclusions: This paper offers a novel use of deep learning for automated lymph node assessment in ultrasound imaging. This system offers a dependable tool for doctors to evaluate lymph node metastases efficiently by fusing sophisticated segmentation techniques with multimodal image processing. It has the potential to greatly enhance patient outcomes and diagnostic accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Clinical Practice: Advancing Medical Imaging Analysis)
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