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Artificial Intelligence and Machine Learning in Clinical Practice: Advancing Medical...
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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: 31 July 2025 | Viewed by 986

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 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. 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 (1 paper)

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Research

23 pages, 12145 KiB  
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
Viewed by 713
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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