Solving data quality issues of fundus images in real-world settings by ophthalmic AI

Zhongwen Li; Wei Chen

doi:10.1016/j.xcrm.2023.100951

Solving data quality issues of fundus images in real-world settings by ophthalmic AI

Cell Rep Med. 2023 Feb 21;4(2):100951. doi: 10.1016/j.xcrm.2023.100951.

Authors

Zhongwen Li¹, Wei Chen²

Affiliations

¹ Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China.
² Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China. Electronic address: chenwei@eye.ac.cn.

Abstract

Liu et al.¹ develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence diagnostics in detecting multiple retinopathies.

MeSH terms

Artificial Intelligence*
Data Accuracy*
Fundus Oculi