Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

9825 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-07-21

Study Completion Date

2024-03-31

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.

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Detailed Description

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Conditions

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Eye Diseases

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Normal participants

Healthy individuals who have no concerns related to their eyes.

No interventions assigned to this group

Patients with Eye-related Chief Complaints

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.

Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases

Intervention Type DIAGNOSTIC_TEST

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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

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.

Minimum Eligible Age

2 Months

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes