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) (LMU ASSIST)

Johannes Schiefelbein

Status

Not yet enrolling

Conditions

Neovascular Age-Related Macular Degeneration (nAMD)

Treatments

Behavioral: AI assisted assessment of SD-OCT scans

Study type

Interventional

Funder types

Other

Industry

Identifiers

NCT06817915

2301LMUASSIST

Details and patient eligibility

About

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.

Full description

This research project at LMU delves into the intersection of artificial augmentation and ophthalmology, specifically focusing on how AI-generated 2nd opinion reports can aid in the treatment planning of neovascular age-related macular degeneration (nAMD). The project will involve a diverse group of up to 1000 ophthalmologists, categorized into six user groups based on their expertise, ranging from residents to seasoned retina specialists.

The core of the research involves assessing the impact of AI-generated 2nd opinion reports on ophthalmologists' treatment decisions for nAMD. Participants will review SD-OCT scans and make initial treatment decisions. Subsequently, they will review AI-generated reports for the same scans and have the opportunity to revise their decisions. This process aims to evaluate the influence of AI insights on clinical judgment.

The project will be conducted virtually, with participants enrolling online from various countries. Data collection will be facilitated through an electronic system, ensuring efficiency and security. Statistical analysis will primarily involve descriptive statistics to summarize the findings. The results of the study will be disseminated through publication in a peer-reviewed journal.

Enrollment

100 estimated patients

Sex

All

Ages

18+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

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 criteria