Development and Validation of Multimodal Deep Learning Model for Autonomous Diagnosis, Generative Reporting, and Specialist Referral in Ophthalmic Diseases: An International Multicenter Cohort Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 2,000
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy of multimodal vision-language model.
研究概览
简要总结
Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools. The purpose of this international, multicenter clinical trial is to developed and validated an agent-based framework that integrates vision-language models and large language models to enhance the diagnostic workflow of anterior segment diseases.
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
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Informed consent obtained;
- •Participants should be sufficiently able to read, write, and understand Chinese or English;
- •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.
研究组 & 干预措施
Normal participants
Healthy individuals who have no concerns related to their eyes.
干预措施: Multimodal Vision-language Model Diagnosis (Diagnostic Test)
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.
干预措施: Multimodal Vision-language Model Diagnosis (Diagnostic Test)
结局指标
主要结局
Diagnostic accuracy of multimodal vision-language model.
时间窗: from July 2025 to September 2025
For each patient, the diagnoses generated by the multimodal vision-language model 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