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網膜画像と屈折データを用いた小児近視進行の深層学習予測

Meng-Tian Kang¹, Yansong Hu², Ningli Wang¹

¹Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases, National Engineering Research Center for Ophthalmology, Engineering Research Center of Ophthalmic Equipment and Materials (Ministry of Education), Capital Medical University, Beijing, China.

JAMA network open

|January 26, 2026

まとめ

新しい深層学習モデルは、網膜画像と屈折データのみを使用して、学童の近視進行と強度近視のリスクを正確に予測します。この方法は、特にリソースが限られた環境での早期介入を可能にします。

科学分野:

眼科学および計算科学
ヘルスケアにおける人工知能
小児視力研究

背景:

小児近視は、不可逆的な視力障害の重大なリスクを伴う、世界的に増加している健康問題です。
近視進行の早期予測は、強度近視とその合併症を防ぐための適時介入に不可欠です。

主な方法:

3048人の6〜9歳の子供が参加した縦断的な学校ベースのコホート研究（Anyang Childhood Eye Study）が実施されました。
畳み込みニューラルネットワーク（CNN）とリカレントニューラルネットワーク（RNN）を組み合わせたDLモデルが開発されました。
モデルのパフォーマンスは、リスク予測の曲線下面積（AUC）および球面等価屈折（SER）予測の平均絶対誤差（MAE）を使用して評価され、多様なコホートで外部検証されました。

結論:

最小限のベースラインデータを使用したDLモデルは、近視および強度近視のリスクを非常に正確に予測します。
このAI駆動型アプローチは、特にサービスが行き届いていない地域での大規模スクリーニングおよび早期介入戦略に有望です。

キーワード:

深層学習近視小児網膜画像屈折予測 AI コンピュータビジョン眼科公衆衛生

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