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Abstract #4450

Development of an artificial intelligence algorithm to automatically assign MR abdomen/pelvis protocols from free-text clinical indications.

Jae Ho Sohn1, Joseph Mesterhazy1, Fouad Al Adel1, Thienkhai Vu1, Alex Rybkin1, and Michael A Ohliger1

1Radiology & Biomedical Imaging, UCSF Medical Center, San Francisco, CA, United States

Timely and accurate MR protocoling is important to ensure best efficiency and diagnostic value in radiology departments. We propose and validate an artificial intelligence based natural language classifier that can assign MR abdomen/pelvis protocols based on free-text clinical indications. We achieve an overall classification accuracy rate of 93% on a test set consisting of 83 free-text clinical indications.

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