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Accurately Identifying Cerebroarterial Stenosis from Angiography Reports Using Natural Language Processing

Ching-Heng Lin1,2,3, Kai-Cheng Hsu3,4,5,6, Chih-Kuang Liang3,7,8,9

  • 1Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.

Diagnostics (Basel, Switzerland)
|August 26, 2022
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Summary

Natural language processing (NLP) models accurately identified intracranial artery stenosis from angiography reports. The XLNet model demonstrated superior performance, offering potential for automatic high-risk patient screening.

Keywords:
cerebrovascular diseasesdeep learningintracranial artery stenosisnatural language processingruled-based model

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Area of Science:

  • Medical informatics
  • Computational linguistics
  • Neurology

Background:

  • Intracranial artery stenosis is a significant risk factor for stroke.
  • Angiography reports contain valuable data for diagnosing cerebrovascular diseases.
  • Current methods for identifying stenosis from reports are underutilized.

Purpose of the Study:

  • To evaluate natural language processing (NLP) techniques for detecting eleven types of intracranial artery stenosis.
  • To compare the performance of rule-based, recurrent neural network (RNN), and XLNet models.

Main Methods:

  • Developed and validated three NLP models: rule-based, RNN (LSTM), and XLNet.
  • Utilized angiography reports from two medical centers for training, internal validation (9614 reports), and external validation (315 reports).
  • Assessed model performance using the area under the receiver operating characteristic curve (AUROC).

Main Results:

  • XLNet achieved the highest performance with an AUROC of 0.97-0.99 for eleven targeted arteries.
  • The rule-based model yielded an AUROC of 0.92-0.96.
  • The RNN model achieved an AUROC of 0.95-0.97.

Conclusions:

  • NLP techniques, particularly XLNet, show promise for automated screening of patients at high risk for intracranial artery stenosis.
  • Challenges remain in model generalization due to variations in report styles and data distribution.