Early Identification and Diagnosis of BAD-related Stroke
Establishment and Validation of a Novel Intelligent Diagnostic Model for BAD-related Stroke Based on the Fusion of Multi-source Clinical Image Information and Its Promotion
Peking Union Medical College Hospital
1,602 participants
Jul 15, 2026
OBSERVATIONAL
Conditions
Summary
Branch atheromatous disease (BAD)-related stroke is an important subtype of acute ischemic stroke involving penetrating arteries and is associated with early neurological deterioration. Early recognition and standardized diagnosis remain challenging in routine clinical practice because clinical symptoms are often non-specific and the diagnosis requires integrated clinical and imaging assessment. This multicenter prospective observational study will collect demographic, clinical, laboratory, electrocardiographic, ultrasound, and multimodal neuroimaging data from adults with acute ischemic stroke within 1 week of symptom onset. Participants will receive routine clinical care determined by their treating physicians; no treatment or management strategy will be assigned by the study protocol. An independent central clinical-imaging adjudication committee will classify participants as BAD-related stroke or non-BAD acute ischemic stroke according to predefined diagnostic criteria. The study aims to develop and externally validate artificial intelligence-assisted screening and diagnostic models for BAD-related stroke and to evaluate their discrimination, calibration, and potential clinical utility.
Eligibility
Plain Language Summary
Simplified for easier understanding
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Interventions
The diagnostic assessment consists of artificial intelligence-assisted analysis of routinely collected clinical, laboratory, cardiovascular, and multimodal neuroimaging data to estimate the probability of BAD-related stroke. The model output will be compared with an independent central clinical-imaging reference diagnosis. The model will not determine treatment assignment in this observational study.
Locations(11)
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NCT07693816