Journal of Digital Imaging

, Volume 31, Issue 2, pp 245–251 | Cite as

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm

  • Hari Trivedi
  • Joseph Mesterhazy
  • Benjamin Laguna
  • Thienkhai Vu
  • Jae Ho Sohn


Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader’s contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.


IBM Watson Machine learning Artificial intelligence Deep learning Natural language processing (NLP) Imaging protocol Workflow efficiency Quality improvement 



Institutional Review Board


Magnetic resonance imaging




Natural language processing


With contrast


Funding Information

HT was supported by an NIH T32 Fellowship, 5T32EB001631-10. JHS was supported by the NVIDIA academic grant program.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

10278_2017_21_MOESM1_ESM.docx (16 kb)
ESM 1 (DOCX 15 kb)


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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.Radiology & Biomedical ImagingUCSF Medical CenterSan FranciscoUSA

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