Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

Study Overview
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Brief Summary
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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.

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

Conditions
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Eye Diseases

Intervention / Treatment
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* Diagnostic Test: Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 2 Months

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Normal participants<br>Healthy individuals who have no concerns related to their eyes. | |
| Patients with Eye-related Chief Complaints<br>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. | Diagnostic Test: Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 |