Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.

ABSTRACT:

Background

The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help address these challenges. We aimed to determine if VA could be estimated using confocal scanning laser ophthalmoscopy (cSLO) imaging and deep learning (DL).

Methods

Snellen corrected VA and cSLO imaging were obtained retrospectively. The Johns Hopkins University (JHU) dataset was used for 10-fold cross-validations and internal testing. The Amsterdam University Medical Centers (AUMC) dataset was used for external independent testing. Both datasets had the same exclusion criteria: visually significant media opacities and images not cent

SUBMITTER: Liu TYA 

PROVIDER: S-EPMC10579177 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature