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NCT Number: NCT07328776

AI vs. Physician for Anti-VEGF Decision-Making: An RCT

We developed an artificial intelligence system, called QiLin, which was designed to assist anti-VEGF treatment decisions in retinal diseases. QiLin was trained and validated via over 20,000 optical coherence tomography images from multicenter datasets, demonstrating strong performance on both internal and external validation. To evaluate its real-world clinical utility, we conducted a randomized controlled trial that rigorously compares the accuracy of treatment decisions between a physician-only arm and an AI-assisted physician arm.

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Key information

Conditions

DME Cardiovascular Diseases Embolism and Thrombosis Eye Diseases Macular Degeneration Neovascular (Wet) Age-related Macular Degeneration Retinal Degeneration Retinal Diseases Retinal Vein Occlusion Retinal Vein Occlusion (RVO) Thrombosis Vascular Diseases Venous Thrombosis

Age range

50 year–85 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Shanghai general hospital, Shanghai, China

Loading trial locations.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Patients with a diagnosis of nAMD, DME, and RVO; Patients who have completed the loading-dose treatment of anti-VEGF agents; Patients who were willing to participate and provided written informed consent.

Exclusion criteria

Refusal to undergo OCT testing; Refusal to complete the 3-month follow-up period; Screening for a history of intraocular surgery within the past 6 months; Subjects with severe systemic diseases, intellectual developmental disorders, psychiatric illnesses, etc.

Treatment and study plan

QiLin-assisted

Other

A Comprehensive Deep Learning Model for Assisting the decision of anti-VEGF therapy: QiLin system

physician only, without QiLin assisted

Other

without QiLin assisted

Primary outcomes

  1. Accuracy of the current anti-VEGF injection decision

    Time frame: At enrollment

    The accuracy of the current anti-VEGF injection decision was defined as the proportion of injection decisions (yes or no) made by the physicians in the two arms that were in agreement with the independent senior expert.

Secondary outcomes

  1. Accuracy of detecting active biomarkers on the current OCT image

    Time frame: At enrollment

    The secondary endpoint was defined as the accuracy of detecting active biomarkers. For each patient, the physician was required to perform a binary classification (present vs. absent) for all of 8 pre-defined active biomarkers (PED, NV, IRF, SRF, SHRM, HRF, DRT or DME, and VMT), and was further confirmed by an independent senior retina specialist. The accuracy for per biomarker was calculated as the proportion of correct classifications for that biomarker, and then the average accuracy was calculated as the secondary endpoint.

Other outcomes

  1. Accuracy of the recommended anti-VEGF treatment interval

    Time frame: 3 months from enrollment

    At enrollment, physicians in both arms will recommend an anti-VEGF treatment interval. Then, patients will attend monthly visits for 3 months. At each visit, an independent expert physician will evaluate whether anti-VEGF injection is required. The accuracy of the recommended treatment interval is defined as the proportion of cases where the recommended treatment interval is concordant with the actual treatment interval.

Study contacts

Contact information is provided by the study sponsor or research team.

Huixun Jia, PhD

CONTACT

[email protected]

86 17853138155

Xiaodong Prof. Sun, PhD

CONTACT

[email protected]

86 17853138155

Sponsors and collaborators

Lead sponsor

Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

Other

Registry information

Official study title

An Artificial Intelligence System for Anti-VEGF Treatment Decisions in Retinal Diseases: A Randomized Controlled Trial

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jan 9, 2026
Registry last updated
May 22, 2026

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.

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