Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

Eye Diseases Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05930444
• Other study ID #: FD-EENT-20230625
• Status: Completed
• Start date: July 21, 2023
• Est. completion date: October 31, 2023

Study information

• Verified date: November 2023
• Source: Eye & ENT Hospital of Fudan University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1673
• Est. completion date: October 31, 2023
• Est. primary completion date: August 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 2 Months and older

Eligibility

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Related Conditions & MeSH terms

• Eye Diseases

Intervention

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.

Locations

Country	Name	City	State
China	The Affiliated Eye Hospital of Nanjing Medical University	Nanjing
China	Fudan Eye & ENT Hospital	Shanghai
China	Suqian First People's Hospital	Suqian

Sponsors (3)

Lead Sponsor	Collaborator
Eye & ENT Hospital of Fudan University	Suqian First People's Hospital, The Affiliated Eye Hospital of Nanjing Medical University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic accuracy of multimodal machine learning program	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.	from July 2023 to October 2023