A deep learning-driven cataract screening model derived from multicenter real-world dataset

This study demonstrates that training on multicenter, real-world data yields a robust and generalizable model, providing a powerful tool for largescale ophthalmic screening. Specifically, our model establishes a methodological blueprint for developing trustworthy medical deep learning systems.

Authors

Yong Zhu (ORCID: https://orcid.org/0000-0002-9806-1965)
Cheng Yu (ORCID: https://orcid.org/0000-0003-1039-1300)
Zhelin Cui (ORCID: https://orcid.org/0009-0004-5515-5322)
Shaohua Pan (ORCID: https://orcid.org/0000-0002-6261-5268)
Weiwei Dai (ORCID: https://orcid.org/0000-0003-3375-0249)

Institutions

Central South University (CN)
Anhui Medical University (CN)
Shandong Eye Institute & Hospital (CN)
Second Affiliated Hospital of Anhui Medical University (CN)
He Eye Hospital (CN)

Publication Details

Journal: Frontiers in Medicine
Published: 2025-11-27
DOI: https://doi.org/10.3389/fmed.2025.1691419
Primary Topic: Retinal Imaging and Analysis
Type: article
Field-Weighted Citation Impact: 0.00

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article

A deep learning-driven cataract screening model derived from multicenter real-world dataset

Yong Zhu, Cheng Yu, Zhelin Cui, Shaohua Pan et al.

Nov 27, 2025

Frontiers in Medicine

Retinal Imaging and Analysis

article

A deep learning-driven cataract screening model derived from multicenter real-world dataset

Yong Zhu, Cheng Yu, Zhelin Cui, Shaohua Pan, Weiwei Dai

November 27, 2025article en

Abstract

This study demonstrates that training on multicenter, real-world data yields a robust and generalizable model, providing a powerful tool for largescale ophthalmic screening. Specifically, our model establishes a methodological blueprint for developing trustworthy medical deep learning systems.

Frontiers in MedicineVol. 12

Central South University (CN), Anhui Medical University (CN), Shandong Eye Institute & Hospital (CN), Second Affiliated Hospital of Anhui Medical University (CN), He Eye Hospital (CN)

Openalex Percentile: Top 48%

Retinal Imaging and Analysis

DOI Open Access PDF OpenAlex

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