Study on Eye-Brain Cross-Organ Mapping and Systemic Disease Association Based on Multimodal Big Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 208,000
- 主要终点
- Performance of Systemic Disease Prediction Models
研究概览
简要总结
This project integrates multimodal eye data (CFP, OCT, OCTA) from 50,000 cases, brain MR data from 150,000 cases, and ICD diagnoses, medical orders, and test results from an eye-brain paired cohort of 8,000 cases to construct an eye-brain cross-modal mapping model and an eye-brain-systemic disease association model. It aims to clarify the quantitative associations between multimodal ocular features and brain structural and vascular characteristics as well as systemic disease ICD diagnoses, thereby uncovering the cross-organ and cross-modal linkage mechanisms between the eye and brain. Ultimately, it seeks to achieve mapping and prediction of brain imaging features based on ocular data, enable ocular-based diagnosis and prediction of various diseases, and contribute significantly to early disease screening, risk stratification, and optimization of clinical diagnosis and treatment.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Eye data: Images must meet quality standards (no significant artifacts, key structures clearly visible), with complete examination records (including examination date and device model).
- •Brain data: MR images must be free of motion artifacts, with complete sequences (at least including T1-weighted imaging and MRA), and reports must confirm no technical issues.
- •Eye-brain paired cohort: Must contain at least one type of eye image (including CFP, OCT, or OCTA) along with brain MR data, and be linkable to a unique identifier and clinical information in the database
- •Clinical data: Clinical information (such as core biochemical indicators, demographic information, and necessary questionnaire items) must have a missing rate ≤30%, with standardized and clear ICD diagnostic codes, complete laboratory test data, and medical order information (including medication or follow-up recommendations)
- •Prior to initial inclusion in the study, participants must have no history of severe organic diseases (including non-curable or end-stage malignancies, severe heart failure (NYHA Class III-IV), end-stage renal disease (CKD Stage 5), decompensated cirrhosis, or significant functional impairment due to severe cerebrovascular disease sequelae, etc.).
排除标准
- •Ocular examinations in which key features cannot be identified due to conditions such as cataracts or vitreous hemorrhage brain MR images deemed unacceptable in quality due to factors such as metal implants
- •Clinical information (including core biochemical indicators, demographic details, essential questionnaire items, etc.) with a missing rate exceeding 30%
- •Individuals who are pregnant or lactating, those with ocular trauma, congenital ocular malformations, or severe organic diseases.
研究组 & 干预措施
Ophthalmic Data Cohort
干预措施: No interventions were involved in this study. (Other)
Cerebral Data Cohort
干预措施: No interventions were involved in this study. (Other)
Oculo-Cerebral Matched Cohort
干预措施: No interventions were involved in this study. (Other)
Clinical Correlation Cohort
干预措施: No interventions were involved in this study. (Other)
结局指标
主要结局
Performance of Systemic Disease Prediction Models
时间窗: From the time of model construction and consistency alignment during the study period to the end of the 12-month follow-up after study initiation.
Age- and sex-stratified Area Under the Receiver Operating Characteristic Curve (AUC)
Model generalisation capability.
时间窗: From the completion of model construction and consistency calibration during the study period to the end of the 12-month follow-up after study initiation.
Evaluated by Cohen's kappa coefficient across external independent cohorts.
Eye-Brain Mapping Model Performance.
时间窗: From the completion of model construction and consistency calibration during the study period to the end of the 12-month follow-up after study initiation.
Measured by correlation coefficient between retinal imaging features and brain neuroimaging metrics.
次要结局
未报告次要终点