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An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI.
K A Wong1, A Hatef1,2, J L Ryu1,3
1From the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio.
AJNR. American Journal of Neuroradiology
|December 15, 2022
Summary
An artificial intelligence tool accurately automates magnetic resonance imaging (MRI) protocolling, reducing physician workload and improving consistency. This AI decision support system shows significant promise for streamlining the imaging protocolling process.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology Informatics
Background:
- Protocolling MRI studies is time-consuming and subject to physician variability.
- Standardizing protocolling is crucial for consistent imaging quality and efficient workflow.
Purpose of the Study:
- To evaluate an AI-based tool for semiautomated MRI protocolling.
- To assess the tool's potential in reducing workload and inter-physician variation.
Main Methods:
- Trained a Long Short-Term Memory network model on 19,721 brain MRI examinations.
- Developed a clinical decision support tool using the AI model.
- Implemented high-confidence predictions for automatic protocol assignment and low-confidence predictions for review.
Main Results:
- The AI model achieved 90.5% accuracy, outperforming manual assignments (85.9%).
- The tool automatically assigned 70% of protocols with 97.3% accuracy.
- For remaining cases, it provided the top 2 protocols with 94.7% accuracy.
Conclusions:
- The AI model demonstrates high accuracy against physician consensus standards.
- The tool effectively reduces workload by automating a significant portion of protocolling.
- This AI decision support system promises to decrease unwarranted variation in MRI protocolling.
