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

ConditionsDME Retinal Vein Occlusion (RVO)Neovascular (Wet) Age-Related Macular Degeneration

Trial statusNot yet recruiting

Trial phaseNot applicable

Trial typeInterventional

Biological sexAll

Age50-85

SponsorShanghai General Hospital, Shanghai Jiao Tong University School of Medicine

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

None

Disqualifiers

None

Trial design

Design model

Parallel

Treatments tested in this trial

QiLin-assisted
Other intervention
A Comprehensive Deep Learning Model for Assisting the decision of anti-VEGF therapy: QiLin system
physician only, without QiLin assisted
Other intervention
without QiLin assisted

Treatment groups

200 Participants

are divided into 2 treatment groups

Group A: QiLin-assisted physician armExperimental treatment 1 intervention

Group B: physician only armActive comparator 1 intervention

Trial outcomes

Primary outcomes

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.

Time frame

At enrollment

Secondary outcomes

Accuracy of detecting active biomarkers on the current OCT image

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.

Time frame

At enrollment

Other outcomes

Accuracy of the recommended anti-VEGF treatment interval

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.

Time frame

3 months from enrollment

Sponsors and contacts

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Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

Lead sponsor

Source ClinicalTrials.gov

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