Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 9,825
- 试验地点
- 3
- 主要终点
- Diagnostic accuracy of multimodal machine learning program
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 2 Months 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Diagnostic accuracy of multimodal machine learning program
时间窗: 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.
次要结局
未报告次要终点