A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.
Journal:
Computer methods and programs in biomedicine
PMID:
35305492
Abstract
BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach to detect shallow ACD using fundus photographs and to identify the hidden features of shallow ACD.