Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study
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View abstract on PubMed
Summary
This summary is machine-generated.AI algorithms for brain aneurysm detection did not improve radiologist performance or reduce reading time in a clinical validation study. Real-world effectiveness and workflow impact require thorough evaluation before AI integration.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Radiology
Background
- Numerous AI algorithms exist for radiological anomaly detection, but clinical integration and validation are infrequent.
- Brain aneurysms are a critical finding in neuroimaging, necessitating accurate and efficient detection methods.
Purpose Of The Study
- To evaluate the clinical applicability and utility of an AI model for brain aneurysm detection.
- To assess the impact of AI assistance on radiologist performance and clinical workflow.
- To compare the diagnostic performance and reading times of radiologists with varying experience levels when assisted by AI.
Main Methods
- Utilized an enlarged open-access Time-Of-Flight Magnetic Resonance Angiography dataset (N=460).
- Trained and validated the AI algorithm on 360 subjects, with 100 subjects in an unseen test set for reading sessions.
- Compared reader performance (sensitivity, false positive rate) and reading times in AI-assisted versus unassisted settings for junior and senior radiologists.
Main Results
- The AI model achieved state-of-the-art performance on the test set (sensitivity=74%, FPR=1.6%).
- Neither junior nor senior readers showed significant improvement in sensitivity when assisted by AI (p=0.59, p=1).
- AI assistance significantly increased reading time for both junior (+15s, p=3x10^-4) and senior (p=3x10^-5) readers, with no change in diagnostic confidence.
Conclusions
- Clinical validation of AI algorithms in real-world settings is crucial.
- Current AI assistance for brain aneurysm detection did not enhance radiologist performance or workflow efficiency.
- The study underscores the need to rigorously examine the practical effectiveness and workflow integration of AI tools before widespread adoption in radiology.
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