[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100646260":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":24,"centralContacts":28,"locations":34,"responsibleParty":148,"collaborators":150,"id":156,"slug":157,"hasResults":158,"nctId":159,"briefTitle":160,"officialTitle":161,"acronym":162,"eligibilityCriteria":163,"healthyVolunteers":158,"sex":164,"minAge":165,"maxAge":166,"enrollmentInfo":167,"targetDuration":170,"studyType":171,"phases":10,"briefSummary":172,"conditions":173,"keywords":177,"overallStatus":37,"whyStopped":10,"lastUpdateSubmitDate":188,"lastUpdatePostDateStruct":189,"startDateStruct":192,"completionDateStruct":194,"leadSponsor":196,"locationsCount":197},{"fullName":5,"class":6},"Peking Union Medical College Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"BAD-related stroke cohort",null,"Adults with acute ischemic stroke who meet predefined clinical-imaging diagnostic criteria for branch atheromatous disease-related stroke after central adjudication.",[13],"Diagnostic Test: Multisource clinical-imaging artificial intelligence diagnostic assessment",{"label":15,"type":10,"description":16,"interventionNames":17},"Non-BAD stroke cohort","Adults with acute ischemic stroke who do not meet diagnostic criteria for BAD-related stroke and are included as the comparator cohort for model development and\u002For external validation.",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":10},"DIAGNOSTIC_TEST","Multisource clinical-imaging artificial intelligence diagnostic assessment","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.",[9,15],[25],{"name":26,"affiliation":5,"role":27},"Jun Ni, MD","PRINCIPAL_INVESTIGATOR",[29],{"name":30,"role":31,"phone":32,"phoneExt":10,"email":33},"Shengde Li, MD","CONTACT","010-69156371","lishengde.medicine@qq.com",[35,52,62,72,82,92,102,112,122,131,141],{"facility":36,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":41,"geoPoint":46,"contacts":47},"Beijing Fangshan District Liangxiang Hospital","RECRUITING","Beijing","China","CN",{"type":42,"coordinates":43},"Point",[44,45],116.39723,39.9075,{"lat":45,"lon":44},[48],{"name":49,"role":31,"phone":50,"phoneExt":10,"email":51},"Qingwei Meng","86 18500626138","mengqingwei1002@sina.com",{"facility":53,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":54,"geoPoint":56,"contacts":57},"Beijing Haidian Hospital",{"type":42,"coordinates":55},[44,45],{"lat":45,"lon":44},[58],{"name":59,"role":31,"phone":60,"phoneExt":10,"email":61},"Zhenghong Zhou","86 18810425636","kongquezzh@126.com",{"facility":63,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":64,"geoPoint":66,"contacts":67},"Beijing Huaxin Hospital (The First Hospital of Tsinghua University)",{"type":42,"coordinates":65},[44,45],{"lat":45,"lon":44},[68],{"name":69,"role":31,"phone":70,"phoneExt":10,"email":71},"Li Wang","86 13611066774","WL0050685@sina.com",{"facility":73,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":74,"geoPoint":76,"contacts":77},"Beijing Jingmei Group General Hospital",{"type":42,"coordinates":75},[44,45],{"lat":45,"lon":44},[78],{"name":79,"role":31,"phone":80,"phoneExt":10,"email":81},"Guixia Fu","86 13488675566","fuguixia-china@163.com",{"facility":83,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":84,"geoPoint":86,"contacts":87},"Beijing Longfu Hospital",{"type":42,"coordinates":85},[44,45],{"lat":45,"lon":44},[88],{"name":89,"role":31,"phone":90,"phoneExt":10,"email":91},"Zhiqiang Yang","86 19520156253","13651227925@126.com",{"facility":93,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":94,"geoPoint":96,"contacts":97},"Beijing Puren Hospital",{"type":42,"coordinates":95},[44,45],{"lat":45,"lon":44},[98],{"name":99,"role":31,"phone":100,"phoneExt":10,"email":101},"Kun Wang","86 13611362026","493107264@qq.com",{"facility":103,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":104,"geoPoint":106,"contacts":107},"Beijing Shijingshan Hospital",{"type":42,"coordinates":105},[44,45],{"lat":45,"lon":44},[108],{"name":109,"role":31,"phone":110,"phoneExt":10,"email":111},"Ying Chen","86 13671189196","cying_217@sina.com",{"facility":113,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":114,"geoPoint":116,"contacts":117},"Beijing Shijitan Hospital, Capital Medical University",{"type":42,"coordinates":115},[44,45],{"lat":45,"lon":44},[118],{"name":119,"role":31,"phone":120,"phoneExt":10,"email":121},"Junfang Ma","86 18001385368","treemjf@163.com",{"facility":123,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":124,"geoPoint":126,"contacts":127},"Beijing Sixth Hospital",{"type":42,"coordinates":125},[44,45],{"lat":45,"lon":44},[128],{"name":129,"role":31,"phone":130,"phoneExt":10,"email":10},"Jingjing Hou","86 13651219645",{"facility":132,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":133,"geoPoint":135,"contacts":136},"Beijing Yanqing District Hospital",{"type":42,"coordinates":134},[44,45],{"lat":45,"lon":44},[137],{"name":138,"role":31,"phone":139,"phoneExt":10,"email":140},"Fei Wang","86 15321208163","2190063233@qq.com",{"facility":5,"status":37,"city":38,"state":10,"zip":10,"country":39,"countryCode":40,"cosmosGeoPoint":142,"geoPoint":144,"contacts":145},{"type":42,"coordinates":143},[44,45],{"lat":45,"lon":44},[146],{"name":30,"role":31,"phone":147,"phoneExt":10,"email":33},"86 010-69156371",{"type":149,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[151,153],{"name":152,"class":6},"Institute of Automation, Chinese Academy of Sciences",{"name":154,"class":155},"Beijing Zhongke Ruiyi Information Technology Co., Ltd.","UNKNOWN","100646260","early-identification-and-diagnosis-of-bad-related-stroke-100646260",false,"NCT07693816","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","SMART-BAD","Inclusion Criteria:\n\n* Age 18 to 80 years.\n* Diagnosis of acute ischemic stroke.\n* Time from symptom onset to enrollment ≤ 1 week; if the onset time is unknown, time from last known well to enrollment ≤ 1 week.\n* Availability of required baseline clinical and neuroimaging assessments according to the study protocol.\n* Written informed consent provided by the participant or legally authorized representative.\n\nParticipants will be classified into the BAD-related stroke cohort if they meet all predefined BAD-related stroke diagnostic criteria, including:\n\n* A single isolated deep subcortical infarct on diffusion-weighted imaging.\n* The presumed culprit perforating artery is the lenticulostriate artery or the paramedian pontine artery.\n* For lenticulostriate artery territory infarction: a comma-shaped lesion extending from inferior to superior direction on coronal DWI or involvement of ≥3 axial DWI slices with 5-7 mm slice thickness.\n* For paramedian pontine artery territory infarction: a lesion extending from the deep pons to the ventral surface of the pons on axial DWI.\n* No ≥50% stenosis of the corresponding parent artery, confirmed by MRA, CTA, or DSA.\n\nParticipants with acute ischemic stroke who do not meet BAD-related stroke criteria will be classified into the non-BAD acute ischemic stroke cohort.\n\nExclusion Criteria:\n\nGeneral exclusion criteria for all participants:\n\n* Intracranial hemorrhage, vascular malformation, aneurysm, brain abscess, malignant intracranial mass, or other non-ischemic intracranial lesion on baseline CT, MRI, MRA, CTA, or DSA.\n* Pre-stroke modified Rankin Scale score ≥2.\n* Life expectancy ≤6 months.\n* Unable to tolerate MRI examination.\n* Pregnancy or breastfeeding.\n* Participation in another clinical study within 3 months before informed consent or current participation in another clinical study that may interfere with this study.\n\nAdditional criteria that preclude classification as BAD-related stroke:\n\n* Ipsilateral extracranial tandem artery stenosis ≥50%.\n* Definite cardioembolic source, including atrial fibrillation, myocardial infarction, clinically significant valvular heart disease, dilated cardiomyopathy, infective endocarditis, atrioventricular conduction disease, or heart rate \\\u003C50 beats\u002Fmin as defined in the protocol.\n* Receipt of or planned acute-phase endovascular treatment after stroke onset.\n* Stroke due to other determined causes, such as moyamoya disease, arterial dissection, or vasculitis.","ALL","18 Years","80 Years",{"count":168,"type":169},1602,"ESTIMATED","90 Days","OBSERVATIONAL","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.\n\nThis 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.",[174,175,176],"Branch Atheromatous Disease","Acute Ischemic Stroke","Cerebral Infarction",[178,179,180,181,182,183,184,185,186,187],"Branch atheromatous disease","Acute ischemic stroke","Artificial intelligence","Machine learning","Deep learning","Diagnostic model","Multimodal imaging","Magnetic resonance imaging","Lenticulostriate artery","Paramedian pontine artery","2026-07-05",{"date":190,"type":191},"2026-07-09","ACTUAL",{"date":193,"type":169},"2026-07-15",{"date":195,"type":169},"2028-12-31",{"name":5,"class":6},11]