Performance of Automated Algorithm in Large and Medium Vessel Occlusion Detection: A Real-World Experience.

Area of Science:

• Neuroscience
• Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Rapid detection of large (LVO) and medium vessel occlusions (MeVO) is critical for acute ischemic stroke management.
• Commercial AI software for automated LVO detection is available, but its performance across different occlusion types and expanding mechanical thrombectomy indications requires further understanding.

Purpose of the Study:

• To evaluate the performance of a commercial, fully automated AI algorithm (Viz.ai) for detecting LVO and MeVO in code stroke patients.
• To assess the algorithm's accuracy in identifying anterior LVO (aLVO), posterior LVO (pLVO), and MeVO.

Main Methods:

• Retrospective analysis of consecutive code stroke patients (March 2020-February 2023) with CTA processed by Viz.ai.
• Comparison of AI detection against a reference standard of radiology reports from 12 board-certified radiologists, adhering to STARD guidelines.
• Primary outcome: accuracy for aLVO; Secondary outcomes: accuracy for all LVO, aLVO with M2-MCA occlusion, and aLVO with all MeVO.

Main Results:

• Of 3576 technically sufficient patients, 616 (17.2%) had vessel occlusions.
• The AI algorithm achieved high sensitivity and specificity for anterior LVO (aLVO): 91% and 93%, respectively.
• Performance was lower for all LVO (73% sensitivity), aLVO with M2-MCA occlusion (74% sensitivity), and aLVO with all MeVO (65% sensitivity), while specificity remained high (≥92%) across these categories.

Conclusions:

• The automated AI algorithm demonstrates high accuracy for anterior LVO detection but has limitations in identifying posterior LVO and MeVO.
• Healthcare teams must recognize the discrepancies between AI findings and actual LVO/MeVO rates for optimal patient triage and timely intervention.