NCT04831333

Deep Learning-based System and AIDS-related Cytomegalovirus Retinitis

Deep Learning-based System for Detection of AIDS-related Cytomegalovirus Retinitis in Ultra-Widefield Fundus Images

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 50 (actual)
• Sponsor: Kuifang Du · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Conditions

• Cytomegalovirus Retinitis

Interventions

Type	Name	Description
OTHER	—

Timeline

• Start date: 2021-04-01
• Primary completion: 2021-05-01
• Completion: 2021-05-01
• First posted: 2021-04-05
• Last updated: 2021-07-21

Locations

1 site across 1 country: China