NCT05930444

Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 9,825 (actual)
• Sponsor: Eye & ENT Hospital of Fudan University · Academic / Other
• Sex: All
• Age: 2 Months
• Healthy volunteers: Accepted

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.

Conditions

• Eye Diseases

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases	Patients presenting with eye-related chief complaints initially complete a mobile phone application. This application utilizes patient medical history and relevant images (such as selfies and photos from eye examinations) to provide intelligent diagnosis. The diagnosis remains undisclosed to the patients. Subsequently, patients seek medical attention and undergo clinical examination by a skilled clinician. The clinical diagnosis is subsequently reviewed by a second experienced clinician. If the diagnoses align, it is considered the gold standard. In cases of discrepancy, the consensus reached by the two clinicians becomes the gold standard.

Timeline

• Start date: 2023-07-21
• Primary completion: 2024-03-10
• Completion: 2024-03-31
• First posted: 2023-07-05
• Last updated: 2024-11-15

Locations

3 sites across 1 country: China