Artificial Intelligence in Biomedical Image Analysis—2nd Edition

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 October 2024 | Viewed by 1092

Special Issue Editors


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Guest Editor
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: biosignaling; bioimaging modeling and computer-assisted functional diagnostic diagnosis systems, including those using CT, MRI, EMG, ECG, EEG, and other physiological signals
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Guest Editor
1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The word “smart”, used frequently in our daily life, is usually associated with some kind of machine learning. Whether we are talking about a smart device or a smart app, machine learning is playing a key role in the background. How smart the app or the device is depends on the selection of the right machine learning technique that will grant the best interpretation of the data and signals involved to achieve the desired outcome. This is mainly why machine learning techniques are progressing rapidly and attracting the attention of researchers and investors, especially in the medical field.

This Special Issue will focus on utilizing machine learning in biomedical image analysis. Researchers are encouraged to submit original research articles or review articles discussing the state-of-the-art machine learning techniques for medical applications, including, but not limited to, the early diagnosis of various kinds of cancer and neurological disorders. The articles are expected to provide an extensive description of the machine learning technique(s) involved, as well as the medical application,  highlighting the performance evaluation metrics used.

Prof. Dr. Ayman El-Baz
Dr. Jasjit S. Suri
Guest Editors

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

  • artificial intelligence
  • biomedical image analysis
  • diagnosis
  • cancer
  • neurological disorders

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Published Papers (1 paper)

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Review

21 pages, 2489 KiB  
Review
Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review
by Katarzyna Kryszan, Adam Wylęgała, Magdalena Kijonka, Patrycja Potrawa, Mateusz Walasz, Edward Wylęgała and Bogusława Orzechowska-Wylęgała
Diagnostics 2024, 14(7), 694; https://doi.org/10.3390/diagnostics14070694 - 26 Mar 2024
Viewed by 962
Abstract
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part [...] Read more.
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the “black-box” nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis—2nd Edition)
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