Tests AI reports on eye scans to see how they affect doctors' treatment choices
Tests AI reports on retinal scans in doctors treating neovascular macular degeneration patients.
Plain-language title generated from the registry record
Registry title: This Project At LMU Looks At How Using AI 2nd Opinion Report to Analyze Retinal Eye Scans Impact Doctors' Decisions About Treatment for Patients with a Specific Eye Disease (nAMD)
Key facts
- Where
- Munich, GermanySee the site
Where in Germany
- LMU KlinikumMunich, GermanyNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Johannes Schiefelbein
- Trial code
- NCT06817915
- Record updated
- 10 February 2025
- Original description
This is a research plan from the University of Munich (LMU) that aims to study how the use of AI reports can impact ophthalmologists' decisions regarding treatment for patients with neovascular age-related macular degeneration (nAMD). This disease is a leading cause of vision loss, and while anti-VEGF treatments are effective, they require careful monitoring and retreatment decisions to maximize benefits.Read full original descriptionShow less
LMU Project on the Impact of Reviewing AI Annotated SD-OCT Therapy Assistance Reports on Ophthalmologists' Treatment Decision-making for Anti-VEGF Therapy in NAMD Patients
This is a research plan from the University of Munich (LMU) that aims to study how the use of AI reports can impact ophthalmologists' decisions regarding treatment for patients with neovascular age-related macular degeneration (nAMD). This disease is a leading cause of vision loss, and while anti-VEGF treatments are effective, they require careful monitoring and retreatment decisions to maximize benefits.
The study will involve up to 1000 ophthalmologists with varying levels of expertise. These ophthalmologists will review SD-OCT scans and make treatment decisions before and after reviewing AI-generated reports. The primary objective is to compare these decisions and see how the AI reports influence them. Secondary objectives include assessing the accuracy and safety of the AI reports.
Inclusion: Electronically consented to the informed consent form (eICF); Criteria to be included in one of the following six Ophthalmology user groups:; Group 1 Non-retina specialist Group: Ophthalmology, completed ophthalmology residence with no or another subspecialty other than retina (e.g., Glaucoma, refractive, etc) Group 2 Resident Group: <5 years in residency in ophthalmology Group 3 Fellow Group: Retina specialist in training Group: in fellowship in vitreoretinal medicine, medical retina Group 4 Retina specialist Group: completed retina training, regular requalification Group 5 Junior reader Group: have already gained experience in the reporting clinical routine with the diagnostics in question and completed the initial certification process at an Image and Reading Center (acc. to centre's SOP) Group 6 Senior reader Group: specialist with several years of experience in the relevant field or have completed at least 3 years of residency training. Completed the certification process at the Image and Reading Center (acc. to centre's SOP).
Exclusion: Not an Ophthalmologist; Does not have time to participate in the estimated project duration of 30 minutes.