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Diagnostics
Special Issues
Advances in Machine Learning for Medical Image Processing and Analysis
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Advances in Machine Learning for Medical Image Processing and Analysis

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: 31 December 2024 | Viewed by 775

Special Issue Editor

Dr. Wei Shao

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: machine learning; multi-omics data fusion; cell imaging; imaging genetics; medical image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the latest advancements at the intersection of machine learning and medical image processing. We highlight the cutting-edge research that leverages the power of AI to improve diagnostic accuracy, enhance therapeutic planning, and revolutionize healthcare. Contributions range from novel algorithms to innovative applications, demonstrating how machine learning techniques are transforming the field of medical image analysis. This Special Issue showcases the progress made in this exciting domain and offers insights into its future potential.

Dr. Wei Shao
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

  • machine learning
  • medical image processing
  • diagnostic accuracy
  • healthcare
  • algorithms

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Published Papers (1 paper)

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Research

10 pages, 1430 KiB  
Article
Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines
by Mahdiar Nekoui, Seyed Ehsan Seyed Bolouri, Amir Forouzandeh, Masood Dehghan, Dornoosh Zonoobi, Jacob L. Jaremko, Brian Buchanan, Arun Nagdev and Jeevesh Kapur
Diagnostics 2024, 14(22), 2526; https://doi.org/10.3390/diagnostics14222526 - 12 Nov 2024
Viewed by 489
Abstract
Background/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where [...] Read more.
Background/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. Methods: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. Results: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. Conclusions: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Medical Image Processing and Analysis)
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