AI vs. Physician for Anti-VEGF Decision-Making: An RCT
An Artificial Intelligence System for Anti-VEGF Treatment Decisions in Retinal Diseases: A Randomized Controlled Trial
1 other identifier
interventional
200
1 country
2
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started May 2026
Shorter than P25 for not_applicable
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 27, 2025
CompletedFirst Posted
Study publicly available on registry
January 9, 2026
CompletedStudy Start
First participant enrolled
May 25, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 1, 2026
CompletedStudy Completion
Last participant's last visit for all outcomes
October 15, 2026
ExpectedMay 22, 2026
December 1, 2025
1 month
December 27, 2025
May 18, 2026
Conditions
Outcome Measures
Primary Outcomes (1)
Accuracy of the current anti-VEGF injection decision
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.
At enrollment
Secondary Outcomes (1)
Accuracy of detecting active biomarkers on the current OCT image
At enrollment
Other Outcomes (1)
Accuracy of the recommended anti-VEGF treatment interval
3 months from enrollment
Study Arms (2)
QiLin-assisted physician arm
EXPERIMENTALphysician only arm
ACTIVE COMPARATORInterventions
A Comprehensive Deep Learning Model for Assisting the decision of anti-VEGF therapy: QiLin system
Eligibility Criteria
You may qualify if:
- 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.
You may not qualify if:
- 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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
Shanghai general hospital, Shanghai Jiao Tong University, Shanghai, 200080
Shanghai, China
Shanghai general hospital
Shanghai, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Xiaodong Sun, PhD
Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- DOUBLE
- Who Masked
- PARTICIPANT, OUTCOMES ASSESSOR
- Purpose
- OTHER
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
December 27, 2025
First Posted
January 9, 2026
Study Start
May 25, 2026
Primary Completion
July 1, 2026
Study Completion (Estimated)
October 15, 2026
Last Updated
May 22, 2026
Record last verified: 2025-12