[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100634346":3},{"organization":4,"outcomesModule":7,"designInfo":24,"detailedDescription":23,"studyPopulation":27,"armGroups":28,"interventions":23,"overallOfficials":35,"centralContacts":39,"locations":23,"responsibleParty":48,"collaborators":52,"id":61,"slug":23,"hasResults":62,"nctId":63,"briefTitle":64,"officialTitle":64,"acronym":23,"eligibilityCriteria":65,"healthyVolunteers":62,"sex":66,"minAge":67,"maxAge":23,"enrollmentInfo":68,"targetDuration":23,"studyType":71,"phases":23,"briefSummary":72,"conditions":73,"keywords":75,"overallStatus":78,"whyStopped":23,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":23},{"fullName":5,"class":6},"Xuanwu Hospital, Beijing","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":23},[9],{"measure":10,"description":11,"timeFrame":12},"Agreement between AI-generated and physician-scored G-FAST scale assessments","The agreement between the scores generated by the artificial intelligence (AI) system and the scores assigned by neurologists on G-FAST scale will be evaluated using weighted Kappa coefficients.","within 7 days of acute stroke onset",[14,17,20],{"measure":15,"description":16,"timeFrame":12},"Agreement of AI System vs. Neurologists in Binary G-FAST Classification (Score ≥3 vs. \u003C3)","Kappa coefficient will be calculated to evaluate the agreement between the artificial intelligence (AI) system and neurologist experts in the binary classification of G-FAST scale scores, defined as high risk (total score ≥3) vs. low risk (total score \\\u003C3) for large vessel occlusion stroke.",{"measure":18,"description":19,"timeFrame":12},"Bland-Altman Agreement Limit Analysis","A Bland-Altman plot will be constructed, with the difference between manual scores and AI scores on the vertical axis and the mean of the two scores on the horizontal axis. The limits of agreement (mean difference ± 1.96 × standard deviation) will be calculated.",{"measure":21,"description":22,"timeFrame":12},"Diagnostic performance analysis","Taking the manual score as the gold standard, a 2×2 contingency table was constructed to calculate the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and Youden index of the AI scoring system for stratifying the G-FAST score (LVO ≥3 vs. non-LVO \\\u003C3). The ROC curve was plotted and the AUC was calculated.",null,{"allocation":23,"interventionModel":23,"interventionModelDescription":23,"primaryPurpose":23,"observationalModel":25,"timePerspective":26,"maskingInfo":23},"COHORT","PROSPECTIVE","Patients with acute ischemic stroke who undergo G-FAST scale assessments using both AI and manual methods.",[29,32],{"label":30,"type":23,"description":31,"interventionNames":23},"AI-first interview group","Participants first undergo G-FAST assessment by AI, followed by G-FAST assessment by human assessors.",{"label":33,"type":23,"description":34,"interventionNames":23},"Human-first group","Participants first undergo G-FAST assessment by human assessors, followed by G-FAST assessment by AI.",[36],{"name":37,"affiliation":5,"role":38},"Qingfeng Ma, MD","STUDY_CHAIR",[40,44],{"name":37,"role":41,"phone":42,"phoneExt":23,"email":43},"CONTACT","+8613601069493","m.qingfeng@163.com",{"name":45,"role":41,"phone":46,"phoneExt":23,"email":47},"Zixin Wang, MD Candidate","+8615031041048","wzx15031041048@163.com",{"type":49,"investigatorFullName":50,"investigatorTitle":51,"investigatorAffiliation":5,"oldNameTitle":23,"oldOrganization":23},"PRINCIPAL_INVESTIGATOR","qingfeng ma","MD",[53,55,57,59],{"name":54,"class":6},"Capital Medical University",{"name":56,"class":6},"People's Hospital of Beijing Daxing District",{"name":58,"class":6},"Beijing Tiantan Hospital",{"name":60,"class":6},"The First Hospital of Fangshan District,Beijing","100634346",false,"NCT07538492","Clinical Validation of an Artificial Intelligence-Based G-FAST Score in Patients With Stroke","Inclusion Criteria:\n\n1. Aged ≥ 18 years, of either sex.\n2. Clinically diagnosed with stroke, and confirmed by cranial CT\u002FMRI to have ischemic or hemorrhagic stroke.\n3. Onset within 7 days.\n4. Alert and oriented, able to cooperate with standardized video and audio data collection.\n5. The patient or their legally authorized representative understands the study and voluntarily provides written informed consent (including consent for audio-visual data collection).\n\nExclusion Criteria:\n\n1. Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).\n2. Patients with impaired consciousness, severe cognitive dysfunction, or psychiatric disorders that prevent cooperation with video collection and scale assessment.\n3. Patients with severe visual or hearing impairment, or global aphasia, who are unable to follow instructions.\n4. Critically ill patients requiring immediate cardiopulmonary resuscitation or endotracheal intubation, making video and audio data collection impossible.\n5. Patients with severe facial or limb deformities, or large-area dressings that severely interfere with camera data collection.\n6. Patients with unilateral or bilateral upper limb amputation, severe deformity, unhealed fracture, joint fixation, or severe contracture.","ALL","18 Years",{"count":69,"type":70},297,"ESTIMATED","OBSERVATIONAL","This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic assessment system for the G-FAST score. The core comparison is the consistency and accuracy between AI-generated G-FAST results and standardized manual G-FAST assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for acute stroke screening and early identification, reduce the subjective variability of manual scoring, and optimize the pre-hospital and in-hospital stroke assessment workflow.",[74],"Stroke",[76,77],"Artificial Intelligence","G-FAST","NOT_YET_RECRUITING","2026-04-15",{"date":81,"type":82},"2026-04-20","ACTUAL",{"date":84,"type":70},"2026-04-10",{"date":86,"type":70},"2028-12-31",{"name":5,"class":6}]