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  9. Natural Language Processing
  11. Abdominal And Pelvic Mri Protocol Prediction Using Natural Language Processing

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.

Journal of Imaging Informatics in Medicine
|January 30, 2025

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View abstract on PubMed

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.

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

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  • 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.
    NLP (natural language processing)
    Protocoling