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Diagnostics
Special Issues
Artificial Intelligence in Ophthalmology
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Artificial Intelligence in Ophthalmology

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: 15 July 2024 | Viewed by 1337

Special Issue Editor

Dr. Zhuoting Zhu

E-Mail Website
Guest Editor
Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
Interests: artificial intelligence in ophthalmology

Special Issue Information

Dear Colleagues,

Ophthalmic diseases pose significant challenges to the medical community, as they can lead to vision impairment or even blindness if not detected and treated in a timely manner. The advent of AI has opened new horizons for ophthalmology, offering innovative solutions to enhance the accuracy and efficiency of ocular disease diagnosis and management. This Special Issue will explore the exciting progress made in using AI technologies to analyze various ocular images, including meibomian gland images, fundus images, and other types of ocular imaging.

The significance of this Special Issue lies in its potential to foster collaboration between researchers, clinicians, and AI experts, leading to transformative discoveries in the early detection and treatment of eye diseases. By combining AI capabilities with cutting-edge imaging techniques, novel biomarkers, and data-driven approaches, ophthalmologists can make more informed decisions and provide personalized treatment strategies for their patients. Moreover, this Special Issue will emphasize the translation of basic research findings into clinical applications, bridging the gap between scientific advancements and real-world healthcare practices.

Dr. Zhuoting Zhu
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

  • artificial intelligence
  • deep learning
  • computer vision
  • ocular imaging
  • cutting-edge technology

Published Papers (1 paper)

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Research

17 pages, 5836 KiB  
Article
Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions
by Wenlong Li, Linbo Bian, Baikai Ma, Tong Sun, Yiyun Liu, Zhengze Sun, Lin Zhao, Kang Feng, Fan Yang, Xiaona Wang, Szyyann Chan, Hongliang Dou and Hong Qi
Diagnostics 2024, 14(2), 121; https://doi.org/10.3390/diagnostics14020121 - 5 Jan 2024
Viewed by 1090
Abstract
Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite [...] Read more.
Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models’ ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen’s kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen’s kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen’s kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape. Full article
(This article belongs to the Special Issue Artificial Intelligence in Ophthalmology)
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