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.
This is a prospective, multicenter, observational study designed to validate the diagnostic performance of an AI-based automated scoring system for the Modified Rankin Scale (mRS) in patients with stroke. The primary objective is to evaluate the agreement between AI-generated mRS scores and standardized manual assessments conducted by trained clinicians. Secondary endpoints include the system's sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in classifying functional outcomes.
Study Type
OBSERVATIONAL
Enrollment
490
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
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
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
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
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.
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
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.
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
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