DMENCT07328776
AI vs. Physician for Anti-VEGF Decision-Making: An RCT
A study testing QiLin-assisted and physician only, without QiLin assisted in people with DME and Retinal Vein Occlusion (RVO), run by Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine at 2 sites in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 50 to 85 years
- Conditions the study is for
- DME, Retinal Vein Occlusion (RVO), Neovascular (Wet) Age-Related Macular Degeneration
- What is being tested?
- QiLin-assisted and physician only, without QiLin assisted
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 2 sitesChina
- Study dates
- 25 May 2026to 15 Oct 2026
- Phase
- Interventional
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites
Shanghai general hospital, Shanghai Jiao Tong University, Shanghai, 200080
Shanghai
Xiaodong Sun, PhD, +86-02163240090, xdsun@sjtu.edu.cn
Shanghai general hospital
Shanghai
Huixun Jia, PhD, 86 17853138155, jiahuixun@sjtu.edu.cn
About this study
Official title: An Artificial Intelligence System for Anti-VEGF Treatment Decisions in Retinal Diseases: A Randomized Controlled 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.
- Being tested
- QiLin-assisted, physician only, without QiLin assisted
- Medical subject headings
- Retinal Vein Occlusion, Macular Degeneration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
QiLin-assistedOther
A Comprehensive Deep Learning Model for Assisting the decision of anti-VEGF therapy: QiLin system
physician only, without QiLin assistedOther
without QiLin assisted
| Study group | Role | What they receive |
|---|---|---|
| QiLin-assisted physician arm | Experimental | Not described Receives: Other: QiLin-assisted |
| physician only arm | Active comparator | Not described Receives: Other: physician only, without QiLin assisted |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At enrollment
Secondary outcomes (1)
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.
Measured: At enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jan 2026
- Study start25 May 2026 (estimated)
- Primary completion1 Jul 2026 (estimated)
- Study completion15 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DMEStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiaodong Sun, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Investigators
- Xiaodong Sun, PhD, Principal investigator, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07328776
- Sponsor's protocol ID
- SXD20251227
- Status last verified
- Dec 2025
- First submitted
- 27 Dec 2025
- Last updated
- 22 May 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided