Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases
- Conditions
- Eye Diseases
- Interventions
- Diagnostic Test: Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases
- Registration Number
- NCT05930444
- Lead Sponsor
- Eye & ENT Hospital of Fudan University
- Brief Summary
With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation. Furthermore, by incorporating patient selfies, eye examination photos, and other image analysis techniques, the diagnostic capabilities can be further enhanced. The multi-center study aims to develop an auxiliary diagnostic program for eye diseases using multimodal machine learning techniques and evaluate its diagnostic efficacy in real-world outpatient clinics.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 9825
- Informed consent obtained;
- Participants should be able to have Chinese as their mother tongue, and be sufficiently able to read, write and understand Chinese;
- For normal participants: individuals should have no concerns related to their eyes.
- For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.
- Incomplete clinical data to support final diagnosis;
- Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Patients with Eye-related Chief Complaints Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases Individuals who have specific concerns or issues related to their eyes, which they consider as the main reason for seeking medical attention or making a complaint.
- Primary Outcome Measures
Name Time Method Diagnostic accuracy of multimodal machine learning program from July 2023 to March 2024 For each patient, the diagnoses generated by the multimodal machine learning program and the clinical diagnosis provided by skilled clinicians were documented and compared. Consistency between the two diagnoses indicates the program's precision in clinical practice.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (3)
The Affiliated Eye Hospital of Nanjing Medical University
🇨🇳Nanjing, China
Suqian First People's Hospital
🇨🇳Suqian, China
Fudan Eye & ENT Hospital
🇨🇳Shanghai, China