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Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.

Joshua D Warner1, Robert P Hartman2, Daniel J Blezek2

  • 1Department of Radiology, University of Wisconsin-Madison School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792-3252, USA. jwarner@uwhealth.org.

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Summary
This summary is machine-generated.

This study developed an AI solution using natural language processing (NLP) to automate MRI exam protocoling. The AI accurately predicts imaging protocols, reducing radiologists' workload and improving efficiency.

Keywords:
AI (artificial/augmented intelligence)BERT (Bidirectional Encoder Representations from Transformers (an NLP model))MRI (magnetic resonance imaging)NLP (natural language processing)Protocoling

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Radiology Informatics

Background:

  • Radiologists face significant workload from non-interpretive tasks like exam protocoling.
  • Automating protocoling can enhance departmental efficiency and reduce radiologist burden.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) artificial intelligence (AI) solution for automated protocoling of abdomen and pelvis MRI exams.
  • To assess the AI model's performance using historical patient metadata and order information.

Main Methods:

  • A retrospective study utilizing de-identified metadata from approximately 46,000 adult abdomen and pelvis MRI exams (2019-2021).
  • Fine-tuning a Bidirectional Encoder Representations from Transformers (BERT) NLP model in sequence classification mode.
  • Excluding 12 months of data during the COVID pandemic to mitigate bias.

Main Results:

  • The trained AI model achieved an accuracy of 88.5% with a Matthews correlation coefficient of 0.874.
  • Expert review of model errors revealed 81.9% were correct or reasonable alternative protocols, yielding a real-world accuracy of 97.9%.

Conclusions:

  • NLP algorithms, including BERT-based models, can effectively predict MRI imaging protocols for the abdomen and pelvis.
  • This AI solution has the potential to significantly decrease non-interpretive task load for radiologists and improve overall departmental efficiency.