The impact of a neuroradiologist on the report of a real-world CT perfusion imaging map derived by AI/ML-driven software

ABSTRACT BACKGROUND AND PURPOSE: According to guideline the computed tomography perfusion (CTP) should read and analysis using computer-aided software. This study evaluates the efficacy of AI/ML (machine learning) -driven software in CTP imaging and the effect of neuroradiologists interpretation of these automated results. MATERIALS AND METHODS: We conducted a retrospective, single-center cohort study from June to December 2023 at a comprehensive stroke center. A total of 132 patients suspected of acute ischemic stroke underwent CTP using. The AI software RAPID.AI was utilized for initial analysis, with subsequent validation and adjustments made by experienced neuroradiologists. The rate of CTP marked as "non reportable", "reportable" and "reportable with correction" by neuroradiologist was recorded. The degree of confidence in the report of basal and angio-CT scan was assessed before and after CTP visualization. Statistical analysis included logistic regression and F1 score assessments to evaluate the predictive accuracy of AI-generated CTP maps RESULTS: The study found that CTP maps derived from AI software were reportable in 65.2% of cases without artifacts, improved to 87.9% reportable cases when reviewed by neuroradiologists. Key predictive factors for artifact-free CTP maps included motion parameters and the timing of contrast peak distances. There was a significant shift to higher confidence scores of the angiographic phase of the CT after the result of CTP CONCLUSIONS: Neuroradiologists play an indispensable role in enhancing the reliability of CTP imaging by interpreting and correcting AI-processed maps. ABBREVIATIONS: CTP=computed tomography perfusion; AI/ML= Artificial Intelligence/Machine Learning; LVO = Large vessel occlusion