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Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

NCT05930444 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 9825

Last updated 2024-11-15

No results posted yet for this study

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

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.

Sponsors & Collaborators

  • The Affiliated Eye Hospital of Nanjing Medical University

    collaborator UNKNOWN

  • Suqian First Hospital

    collaborator OTHER

  • Eye & ENT Hospital of Fudan University

    lead OTHER

Eligibility

Min Age
2 Months
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-07-21
Primary Completion
2024-03-10
Completion
2024-03-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05930444 on ClinicalTrials.gov