This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic scoring system for the Modified Rankin Scale (mRS). The core comparison is the consistency and accuracy between the AI-generated scores and standardized manual mRS follow-up assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for stroke prognosis assessment, reduce the subjective variability of manual scoring, and optimize the stroke follow-up workflow.
Age ≥ 18 years, male or female.
Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have stroke.
Clinically stable, with basic communication ability at discharge or outpatient visit. The patient or a fixed family caregiver is able to cooperate with telephone follow-up at 1 week after discharge or outpatient visit.
Signed informed consent by the patient or their legally authorized representative.
Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
Presence of severe disturbance of consciousness, severe cognitive impairment, psychiatric disorders, or global aphasia at discharge/outpatient visit, preventing effective communication; neither the patient nor family can cooperate with follow-up or assessment.
Combined with severe multi-organ failure (e.g., cardiac, hepatic, renal, respiratory), with an expected survival of less than 1 month, making completion of the 1-week follow-up impossible.
Long-term bedridden without a fixed caregiver, with no confirmed contact for follow-up, or refusal to participate in telephone follow-up and mRS assessment.
Consecutive stroke patients from multiple centers who are ≥18 years old, with confirmed stroke, and able to complete the 1-week follow-up will be enrolled. Patients with severe non-stroke neurological diseases, inability to communicate, or loss to follow-up will be excluded.
Cohort
Prospective
Not listed
Agreement Between Artificial Intelligence (AI)-Based and Manual Modified Rankin Scale (mRS) Assessments
The weighted kappa coefficient quantifies the level of agreement between the Artificial Intelligence (AI)-generated Modified Rankin Scale (mRS) scores and the standardized manual mRS assessments performed by trained clinicians
Agreement Between AI-based and Manual Assessments of Dichotomized Modified Rankin Scale (mRS)
The simple kappa coefficient quantifies the level of agreement between the Artificial Intelligence (AI)-generated dichotomized Modified Rankin Scale (mRS) scores (0-2 vs. 3-6) and the standardized manual mRS assessments performed by trained clinicians
Bland-Altman Limits of Agreement Between AI and Manual Modified Rankin Scale (mRS) Scores
The Bland-Altman limits of agreement analysis evaluates the consistency between the Artificial Intelligence (AI)-generated and manually assessed Modified Rankin Scale (mRS) scores. The difference between manual and AI scores will be plotted on the y-axis against their mean on the x-axis, with limits of agreement (mean difference ± 1.96 × standard deviation) calculated. The analysis aims to visually assess how agreement varies across the range of mRS scores and identify any proportional bias, such as greater disagreement in patients with severe disability.
Diagnostic Performance of AI-Based vs. Manual Modified Rankin Scale (mRS) Dichotomization
The diagnostic performance analysis evaluates the ability of the Artificial Intelligence (AI)-based Modified Rankin Scale (mRS) scoring system to classify functional outcomes, using manual assessment as the reference standard. A 2×2 contingency table will be constructed for the dichotomized mRS categories (good outcome: 0-2 vs. poor outcome: 3-6). The analysis will calculate sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and Youden's index. A receiver operating characteristic (ROC) curve will be plotted, and the area under the curve (AUC) will be computed.
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Xuanwu Hospital, Beijing
Lead sponsor
Capital Medical University
Collaborator
People's Hospital of Beijing Daxing District
Collaborator
Beijing Tiantan Hospital
Collaborator
The First Hospital of Fangshan District,Beijing
Collaborator
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