Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.

ABSTRACT:

Background

Ophthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain.

Methods

In this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and no

SUBMITTER: Du K 

PROVIDER: S-EPMC11128864 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature