Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms

JaeSeong Hong; Sangchul Yoon; Kyu‐Won Shim; Yu Rang Park

doi:10.1161/strokeaha.123.044026

January 23, 2024StrokeOpen Access

Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms

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Key Points

Evaluate whether deep learning algorithms trained on retinal photographs can serve as viable noninvasive screening and staging biomarkers for Moyamoya disease.
Developed and validated deep learning classification models using retinal photograph datasets.
Trained algorithms to evaluate ocular vascular biomarkers for screening presence and determining clinical stages of Moyamoya disease.
Retinal photographs functioned as viable diagnostic biomarkers for identifying Moyamoya disease.
The deep learning algorithm successfully categorized the disease into distinct clinical stages.

Implication

Demonstrates that deep learning algorithms can screen and stage Moyamoya disease using retinal photographs, highlighting the utility of ocular biomarkers.

Abstract

This study demonstrated that retinal photographs could be used as potential biomarkers for screening and staging of MMD and the disease stage could be classified by a deep learning algorithm.

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Cite This Study

Hong et al. (2024) studied this question.

synapsesocial.com/papers/69da7e92114855a512684195 https://doi.org/10.1161/strokeaha.123.044026

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Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms

Key Points

Implication

Abstract

Discussion

Cite This Study

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