Deep Learning-based System and AIDS-related Cytomegalovirus Retinitis

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.

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Key information

Conditions

Cytomegalovirus Retinitis Acquired Immunodeficiency Syndrome Blood-Borne Infections Communicable Diseases Cytomegalovirus Infections DNA Virus Infections Eye Diseases Eye Infections Eye Infections, Viral Genital Diseases HIV Infections Herpesviridae Infections Immune System Diseases Immunologic Deficiency Syndromes Infections Lentivirus Infections RNA Virus Infections Retinal Diseases Retinitis Retroviridae Infections Sexually Transmitted Diseases Sexually Transmitted Diseases, Viral Slow Virus Diseases Urogenital Diseases Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Youan Hospital, Capital Medical University

Beijing, Beijing Municipality, 100069, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

The UWF images from HIV/AIDS patients.

Exclusion criteria

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

Treatment and study plan

Primary outcomes

Evaluating the applicability of the DL system to identify AIDS-related CMVR

Time frame: 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.

Sponsors and collaborators

Lead sponsor

Kuifang Du

Other

Collaborators

Beijing Tongren Hospital

Registry information

Official study title

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

Important dates

Study start: 2021
Primary completion: 2021
Study completion: 2021
First posted: Apr 5, 2021
Registry last updated: Jul 21, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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