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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04831333

Enrollment

Registered

2021-04-05

Start date

2021-04-01

Completion date

2021-05-01

Last updated

2021-07-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cytomegalovirus Retinitis

Keywords

AIDS, HIV, Cytomegalovirus Retinitis, Deep learning, Ultra-wide-field imaging

Brief 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.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

CROSS_SECTIONAL

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

The UWF images from HIV/AIDS patients.

Exclusion criteria

1. The UWF images would be excluded if all three human graders gave different diagnosis. 2. The UWF images with poor quality would be excluded.

Design outcomes

Primary

Measure	Time frame	Description
Evaluating the applicability of the DL system to identify AIDS-related CMVR	April 2021	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.

Countries

China

Outcome results