Applications of artificial intelligence to inherited retinal diseases: A systematic review

Mohamad Issa; Georges Sukkarieh; Mathias Gallardo; Ilias Sarbout; Sophie Bonnin; Ramin Tadayoni; Dan Milea

doi:10.1016/j.survophthal.2024.11.007

Applications of artificial intelligence to inherited retinal diseases: A systematic review

Mohamad Issa
, Georges Sukkarieh
, Mathias Gallardo
, Ilias Sarbout
, Sophie Bonnin
, Ramin Tadayoni
, Dan Milea^*

^*Corresponding author for this work

Research output: Contribution to journal › Review › peer-review

Abstract

Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limited availability of retinal subspecialists, genetic testing and genetic counseling, there is a high need for accurate and accessible diagnostic methods. The currently available AI studies, aiming for detection, classification, and prediction of IRD, remain mainly retrospective and include relatively limited numbers of patients due to their scarcity. We summarize the latest findings and clinical implications of machine-learning algorithms in IRD, highlighting the achievements and challenges of AI to assist ophthalmologists in their clinical practice.

Original language	English
Journal	Survey of Ophthalmology
Volume	70
Issue number	2
Pages (from-to)	255-264
ISSN	0039-6257
DOIs	https://doi.org/10.1016/j.survophthal.2024.11.007
Publication status	Published - 2025

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Inc.

Keywords

Artificial intelligence
Deep-learning
Inherited retinal diseases
Machine-learning
Retinal dystrophies
Retinitis pigmentosa

Access to Document

10.1016/j.survophthal.2024.11.007

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Cite this

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title = "Applications of artificial intelligence to inherited retinal diseases: A systematic review",

abstract = "Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limited availability of retinal subspecialists, genetic testing and genetic counseling, there is a high need for accurate and accessible diagnostic methods. The currently available AI studies, aiming for detection, classification, and prediction of IRD, remain mainly retrospective and include relatively limited numbers of patients due to their scarcity. We summarize the latest findings and clinical implications of machine-learning algorithms in IRD, highlighting the achievements and challenges of AI to assist ophthalmologists in their clinical practice.",

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author = "Mohamad Issa and Georges Sukkarieh and Mathias Gallardo and Ilias Sarbout and Sophie Bonnin and Ramin Tadayoni and Dan Milea",

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T2 - A systematic review

AU - Issa, Mohamad

AU - Sukkarieh, Georges

AU - Gallardo, Mathias

AU - Sarbout, Ilias

AU - Bonnin, Sophie

AU - Tadayoni, Ramin

AU - Milea, Dan

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N2 - Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limited availability of retinal subspecialists, genetic testing and genetic counseling, there is a high need for accurate and accessible diagnostic methods. The currently available AI studies, aiming for detection, classification, and prediction of IRD, remain mainly retrospective and include relatively limited numbers of patients due to their scarcity. We summarize the latest findings and clinical implications of machine-learning algorithms in IRD, highlighting the achievements and challenges of AI to assist ophthalmologists in their clinical practice.

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KW - Artificial intelligence

KW - Deep-learning

KW - Inherited retinal diseases

KW - Machine-learning

KW - Retinal dystrophies

KW - Retinitis pigmentosa

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