Ophthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important. However, the manual screening with fundus imaging is laborious and subjective. Deep learning (DL) system has been developed for the automated detection of various eye diseases with high accuracy and efficiency, including diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), papilledema, lattice degeneration and retinal breaks, from ocular fundus photographs. UWF imaging is a relatively new imaging modality for DL system but has also shown extraordinary talents in automatic retinal analysis With the press for routine CMVR screening in AIDS patients and the great capacity of DL system, the use of deep learning (DL) system to AIDS-related CMVR with Ultra-Widefield (UWF) fundus images is promising. The investigators previously developed a DL system to detect AIDS-related CMVR. For further evaluating the applicability of the DL system, a prospective dataset is needed.
Study Type
OBSERVATIONAL
Enrollment
50
Beijing Youan Hospital, Capital Medical University
Beijing, Beijing Municipality, China
Evaluating the applicability of the DL system to identify AIDS-related CMVR
The investigators compared the performance between two trained (senior and junior) retinal ophthalmologists with the DL system. A senior retinal ophthalmologist and a junior retinal ophthalmologist were asked to independently screen the UWF images in the prospective dataset. Accuracy, sensitivity and specificity were used to evaluate the performance.
Time frame: April 2021
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