Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

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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Key information

Conditions

Eye Diseases

Age range

2 month and older

Sex eligibility

All sexes

Study type

Observational

Primary location

The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases

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.

Primary outcomes

Diagnostic accuracy of multimodal machine learning program

Time frame: from July 2023 to March 2024

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.

Sponsors and collaborators

Lead sponsor

Eye & ENT Hospital of Fudan University

Other

Collaborators

Suqian First Hospital
The Affiliated Eye Hospital of Nanjing Medical University

Registry information

Official study title

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

Important dates

Study start: 2023
Primary completion: 2024
Study completion: 2024
First posted: Jul 5, 2023
Registry last updated: Nov 15, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed