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Artificial Intelligence in Eye Disease—3rd Edition

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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: 30 June 2024 | Viewed by 7432

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Special Issue Editor

Prof. Dr. Jae-Ho Han

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Guest Editor

1. Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Republic of Korea
2. Department of Artificial Intelligence, Korea University, Seoul 136-701, Republic of Korea
Interests: artificial intelligence in biomedicine; diagnosis of retinal diseases; deep learning for ophthalmology images; neuroscience research
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Special Issue Information

Dear Colleagues,

While the use of artificial intelligence (AI) is rapidly spreading to the medical world amid the vortex of the Fourth Industrial Revolution, the use of AI in ophthalmology is attracting attention for the diagnosis of various ophthalmic diseases, including optic nerve diseases, which are difficult to diagnose. In particular, introducing AI could help to diagnose with high accuracy when applied to fundus photographs, optical coherence tomography, and the visual field to achieve strong classification performance in the detection of occular and retinal diseases. In occular imaging, AI can be used as a possible solution for screening, diagnosing, and monitoring patients with major eye diseases in primary care and community settings. For instance, through deep learning algorithms that read retinal images, various diseases can be observed, such as bleeding, macular abnormalities—e.g., drusen—choroidal abnormalities, retinal vessel abnormalities, nerve fiber layer defects, and glaucomatous optic nerve papilla changes. Thus, deep learning architectures can be applied to learn to recognize eye diseases, thereby raising the diagnosis rate with a clinically acceptable performance. In other words, AI serves as a safety device for both patients and doctors, as well as an auxiliary tool to quickly judge the results. It prevents the possibility of misdiagnosis that can occur in the first place, provides treatment efficiency, and increases patient reliability. Consequently, AI could potentially revolutionize the way that ophthalmology is practiced in the future. Thus, the aim of this Special Issue is to highlight the recent progress and trends in utilizing AI techniques, such as machine learning and deep learning for detecting, screening, diagnosing, and monitoring numerous eye diseases, not only in diverse clinical practice but also in basic research on ophthalmology.

Prof. Dr. Jae-Ho Han
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

medical diagnosis
artificial intelligence
deep learning
fundus image
optical coherence tomography
ophthalmology
retinal vessel
glaucoma
retinopahty
macular degeneration
image segmentation

Related Special Issues

Artificial Intelligence in Eye Disease in Diagnostics (12 articles)
Artificial Intelligence in Eye Disease – Volume 2 in Diagnostics (10 articles)

Published Papers (5 papers)

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Research

16 pages, 4577 KiB

Open AccessArticle

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration

by Mariana Miranda, Joana Santos-Oliveira, Ana Maria Mendonça, Vânia Sousa, Tânia Melo and Ângela Carneiro

Diagnostics 2024, 14(10), 975; https://doi.org/10.3390/diagnostics14100975 - 8 May 2024

Viewed by 293

Abstract

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches. Full article

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease—3rd Edition)

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18 pages, 2832 KiB

Open AccessArticle

A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique

by Abdul Rahaman Wahab Sait

Diagnostics 2023, 13(19), 3120; https://doi.org/10.3390/diagnostics13193120 - 3 Oct 2023

Cited by 2 | Viewed by 1961

Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes. It affects a large portion of the population of the Kingdom of Saudi Arabia. Existing systems assist clinicians in treating DR patients. However, these systems entail significantly high computational costs. In addition, dataset imbalances may lead existing DR detection systems to produce false positive outcomes. Therefore, the author intended to develop a lightweight deep-learning (DL)-based DR-severity grading system that could be used with limited computational resources. The proposed model followed an image pre-processing approach to overcome the noise and artifacts found in fundus images. A feature extraction process using the You Only Look Once (Yolo) V7 technique was suggested. It was used to provide feature sets. The author employed a tailored quantum marine predator algorithm (QMPA) for selecting appropriate features. A hyperparameter-optimized MobileNet V3 model was utilized for predicting severity levels using images. The author generalized the proposed model using the APTOS and EyePacs datasets. The APTOS dataset contained 5590 fundus images, whereas the EyePacs dataset included 35,100 images. The outcome of the comparative analysis revealed that the proposed model achieved an accuracy of 98.0 and 98.4 and an F1 Score of 93.7 and 93.1 in the APTOS and EyePacs datasets, respectively. In terms of computational complexity, the proposed DR model required fewer parameters, fewer floating-point operations (FLOPs), a lower learning rate, and less training time to learn the key patterns of the fundus images. The lightweight nature of the proposed model can allow healthcare centers to serve patients in remote locations. The proposed model can be implemented as a mobile application to support clinicians in treating DR patients. In the future, the author will focus on improving the proposed model’s efficiency to detect DR from low-quality fundus images. Full article

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease—3rd Edition)

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18 pages, 11352 KiB

Open AccessArticle

A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography

by Matteo Interlenghi, Giancarlo Sborgia, Alessandro Venturi, Rodolfo Sardone, Valentina Pastore, Giacomo Boscia, Luca Landini, Giacomo Scotti, Alfredo Niro, Federico Moscara, Luca Bandi, Christian Salvatore and Isabella Castiglioni

Diagnostics 2023, 13(18), 2965; https://doi.org/10.3390/diagnostics13182965 - 15 Sep 2023

Viewed by 830

Abstract

The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector. Two-hundred and twenty six UWF-FRTs were retrospectively collected from two centres and manually annotated to train and test the algorithms. Notably, the combination of the ML-based radiomics model and the DL-based macular detector reported 93% sensitivity and 74% specificity when applied to the data of the centre used for external testing, capturing explainable features associated with drusen or pigmentary abnormalities. In comparison to the human operator’s annotations, the system yielded a 0.79 Cohen κ, demonstrating substantial concordance. To our knowledge, these results are the first provided by a radiomic approach for AMD supporting the suitability of an explainable feature extraction method combined with ML for UWF-FRT. Full article

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease—3rd Edition)

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20 pages, 11065 KiB

Open AccessArticle

Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features

by Mohammed Alshahrani, Mohammed Al-Jabbar, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed and Jamil Abdulhamid Mohammed Saif

Diagnostics 2023, 13(17), 2783; https://doi.org/10.3390/diagnostics13172783 - 28 Aug 2023

Cited by 1 | Viewed by 2815

Abstract

Diabetic retinopathy (DR) is a complication of diabetes that damages the delicate blood vessels of the retina and leads to blindness. Ophthalmologists rely on diagnosing the retina by imaging the fundus. The process takes a long time and needs skilled doctors to diagnose and determine the stage of DR. Therefore, automatic techniques using artificial intelligence play an important role in analyzing fundus images for the detection of the stages of DR development. However, diagnosis using artificial intelligence techniques is a difficult task and passes through many stages, and the extraction of representative features is important in reaching satisfactory results. Convolutional Neural Network (CNN) models play an important and distinct role in extracting features with high accuracy. In this study, fundus images were used for the detection of the developmental stages of DR by two proposed methods, each with two systems. The first proposed method uses GoogLeNet with SVM and ResNet-18 with SVM. The second method uses Feed-Forward Neural Networks (FFNN) based on the hybrid features extracted by first using GoogLeNet, Fuzzy color histogram (FCH), Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP); followed by ResNet-18, FCH, GLCM and LBP. All the proposed methods obtained superior results. The FFNN network with hybrid features of ResNet-18, FCH, GLCM, and LBP obtained 99.7% accuracy, 99.6% precision, 99.6% sensitivity, 100% specificity, and 99.86% AUC. Full article

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease—3rd Edition)

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20 pages, 9144 KiB

Open AccessArticle

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

by Aditya Tripathi, Preetham Kumar, Akshat Tulsani, Pavithra Kodiyalbail Chakrapani, Geetha Maiya, Sulatha V. Bhandary, Veena Mayya, Sameena Pathan, Raghavendra Achar and U. Rajendra Acharya

Diagnostics 2023, 13(15), 2550; https://doi.org/10.3390/diagnostics13152550 - 31 Jul 2023

Cited by 1 | Viewed by 960

Abstract

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME. Full article

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease—3rd Edition)

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