Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Normal participants Healthy individuals who have no concerns related to their eyes. |
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| 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. |
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.
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Outcome Measures
Primary Outcome Measures
- Diagnostic accuracy of multimodal machine learning program [from July 2023 to October 2023]
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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Informed consent obtained;
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Participants should be able to have Chinese as their mother tongue, and be sufficiently able to read, write and understand Chinese;
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For normal participants: individuals should have no concerns related to their eyes.
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For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.
Exclusion Criteria:
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Incomplete clinical data to support final diagnosis;
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Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The Affiliated Eye Hospital of Nanjing Medical University | Nanjing | China | ||
| 2 | Fudan Eye & ENT Hospital | Shanghai | China | ||
| 3 | Suqian First People's Hospital | Suqian | China |
Sponsors and Collaborators
- Eye & ENT Hospital of Fudan University
- The Affiliated Eye Hospital of Nanjing Medical University
- Suqian First People's Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- FD-EENT-20230625