Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Varela, Malena Daich; Sen, Sagnik; Guimarães, Thales Antonio Cabral de; Kabiri, Nathaniel; Pontikos, Nikolas; Balaskas, Konstantinos; Michaelides, Michel

doi:10.1007/s00417-023-06052-x

Cited by 27 publications

(7 citation statements)

References 149 publications

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“…Furthermore, AI systems are only as good as the data used to train them. If the data used to train the AI system does not adequately represent the diversity of the population, then the AI system will produce biased outcomes [ 1 , 44 , 45 ]. For example, if the data used to train the AI system comes predominantly from Caucasian patients, then the AI system may be less accurate in detecting eye diseases in other ethnicities.…”

Section: Resultsmentioning

confidence: 99%

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology

Veritti,

Rubinato,

Sarao

et al. 2023

Graefes Arch Clin Exp Ophthalmol

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Purpose This narrative review aims to provide an overview of the dangers, controversial aspects, and implications of artificial intelligence (AI) use in ophthalmology and other medical-related fields. Methods We conducted a decade-long comprehensive search (January 2013–May 2023) of both academic and grey literature, focusing on the application of AI in ophthalmology and healthcare. This search included key web-based academic databases, non-traditional sources, and targeted searches of specific organizations and institutions. We reviewed and selected documents for relevance to AI, healthcare, ethics, and guidelines, aiming for a critical analysis of ethical, moral, and legal implications of AI in healthcare. Results Six main issues were identified, analyzed, and discussed. These include bias and clinical safety, cybersecurity, health data and AI algorithm ownership, the “black-box” problem, medical liability, and the risk of widening inequality in healthcare. Conclusion Solutions to address these issues include collecting high-quality data of the target population, incorporating stronger security measures, using explainable AI algorithms and ensemble methods, and making AI-based solutions accessible to everyone. With careful oversight and regulation, AI-based systems can be used to supplement physician decision-making and improve patient care and outcomes.

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Section: Resultsmentioning

confidence: 99%

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology

Veritti,

Rubinato,

Sarao

et al. 2023

Graefes Arch Clin Exp Ophthalmol

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“…These figures are of the same order of magnitude as those that allowed FDA approval of an AI algorithm aimed at diagnosing diabetic retinopathy (IDx-DR: specificity of 87.2% and sensitivity of 90.7%). [19][20][21] This suggests that our AI model may similarly be of value to the health care systems. In terms of grading vitritis into one of the six SUN grades (objective 2), the model showed limited performances (global accuracy of 0.61) in keeping with the low number of images representing the highest grades of vitritis (especially grade 3+ [N = 94] and grade 4+ [N = 59+]).…”

Section: Objective 4: Validation Studymentioning

confidence: 91%

“…The 649 images representing patients with vitritis was a fairly high number when we compare this study with the existing literature on other rare diseases. 19 In fact, unlike diabetic retinopathy, uveitis is a rare disease, and it is fairly impossible to gather millions of images. Increasing this number would therefore need an international and lengthy multicentric effort that we are currently trying to put together.…”

Section: Objective 4: Validation Studymentioning

confidence: 99%

Automated detection of vitritis using ultrawide-field fundus photographs and deep learning

Mhibik,

Kouadio,

Jung

et al. 2024

Retina

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Purpose: Evaluate the performance of a deep learning (DL) algorithm for the automated detection and grading of vitritis on ultra-wide field (UWF) imaging. Design: Cross-sectional non-interventional study. Method: UWF fundus retinophotographs of uveitis patients were used. Vitreous haze was defined according to the 6 steps of the SUN classification. The DL framework TensorFlow and the DenseNet121 convolutional neural network were used to perform the classification task. The best fitted model was tested in a validation study. Results: 1181 images were included. The performance of the model for the detection of vitritis was good with a sensitivity of 91%, a specificity of 89%, an accuracy of 0.90 and an area under the ROC curve of 0.97. When used on an external set of images, the accuracy for the detection of vitritis was 0.78. The accuracy to classify vitritis in one of the 6 SUN grades was limited (0.61), but improved to 0.75 when the grades were grouped in three categories. When accepting an error of one grade, the accuracy for the 6-class classification increased to 0.90, suggesting the need for a larger sample to improve the model performances. Conclusion: We describe a new DL model based on UWF fundus imaging that produces an efficient tool for the detection of vitritis. The performance of the model for the grading into 3 categories of increasing vitritis severity was acceptable. The performance for the 6-class grading of vitritis was limited but can probably be improved with a larger set of images.

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“…The development and validation of AI-powered retinal diseases prioritization tools illustrate this trend. These tools are assessed through rigorous metrics including accuracy, sensitivity, specificity, and area under the curve (AUC), underscoring the precision and reliability of AI in ophthalmology [ 39 ].…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

A Beginner’s Guide to Artificial Intelligence for Ophthalmologists

Kang,

Wu,

Yuan

et al. 2024

Ophthalmol Ther

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The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

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Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Cited by 27 publications

References 149 publications

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology

Automated detection of vitritis using ultrawide-field fundus photographs and deep learning

A Beginner’s Guide to Artificial Intelligence for Ophthalmologists

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