[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"hematoma-expansion\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:hematoma-expansion":25},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":26,"overallStatus":28,"whyStopped":4,"lastUpdateSubmitDate":29,"lastUpdatePostDateStruct":30,"startDateStruct":33,"completionDateStruct":35,"leadSponsor":37,"locationsCount":5},"100562357","noncontrast-ct-based-deep-learning-for-predicting-hematoma-expansion-risk-in-patients-with-spontaneous-intracerebral-hemorrhage-100562357",false,"NCT06602115","Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage","NCCT-DL-HE","Inclusion Criteria:\n\n1. Primary, spontaneous (non-traumatic) intracerebral hemorrhage (ICH).\n2. Age ≥ 18 years.\n3. Baseline CT performed within 24 hours of ICH symptom onset or last seen well (LSW).\n4. Follow-up CT within 72 hours.\n\nExclusion Criteria:\n\n1. Secondary ICH caused by trauma, vascular anomalies (e.g., aneurysm, cavernous angioma, arteriovenous malformation), brain tumor, or hemorrhagic transformation in brain infarction.\n2. Primary intraventricular hemorrhage (IVH).\n3. Surgical treatment with external ventricular drain placement or craniotomy.\n4. Obvious artifacts observed in CT images.","ALL","18 Years",{"count":19,"type":20},2000,"ESTIMATED","OBSERVATIONAL","Hematoma expansion is an independent predictor of poor prognosis and early neurological deterioration in patients with spontaneous intracerebral hemorrhage. Early identification of high-risk patients and timely targeted medical interventions may provide a crucial opportunity to limit hematoma growth and improve neurological outcomes. This study aims to develop an end-to-end deep learning model based on noncontrast computed tomography images to predict the risk of hematoma expansion in patients with spontaneous intracerebral hemorrhage. This model could serve as a valuable risk stratification tool for patients with hematoma expansion, facilitating targeted treatment and providing clinicians with streamlined decision-making support in emergency situations.",[24,25],"Spontaneous Intracerebral Hemorrhage","Hematoma Expansion",[24,25,27],"Deep Learning","NOT_YET_RECRUITING","2024-09-17",{"date":31,"type":32},"2024-09-19","ACTUAL",{"date":34,"type":20},"2024-09-25",{"date":36,"type":20},"2024-12",{"name":38,"class":39},"Qiang Yu","OTHER"]