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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization.

Imon Banerjee1, Hailye H Choi2, Terry Desser2

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We developed an automated method to determine Liver Imaging Reporting and Data System (LI-RADS) scores from ultrasound reports. This approach works for both structured and unstructured reports, aiding in hepatocellular carcinoma screening and AI research.

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Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Ultrasound (US) reports are crucial for diagnosing liver lesions.
  • Accurate Liver Imaging Reporting and Data System (LI-RADS) categorization is vital for patient management.
  • Manual LI-RADS assessment can be time-consuming and prone to variability.

Purpose of the Study:

  • To develop a scalable, automated approach for inferring LI-RADS final assessment categories from narrative US reports.
  • To enable LI-RADS scoring for both structured and unstructured reports, including historical data.
  • To facilitate large-scale data mining for AI-based healthcare research.

Main Methods:

  • A computerized approach was developed for large-scale inference of LI-RADS categories.
  • The model was trained on automatically extracted LI-RADS scores from structured reports.
  • The system demonstrated the ability to infer LI-RADS scores from unstructured reports, even those predating LI-RADS guidelines.
  • No human-labeled data was utilized in the training or inference process.

Main Results:

  • The proposed model successfully inferred LI-RADS final assessment categories from narrative US reports.
  • The approach proved effective for both template-based and pre-guideline unstructured reports.
  • Automated extraction of LI-RADS scores from structured reports served as a basis for training the inference model.

Conclusions:

  • The automated LI-RADS categorization approach offers a scalable solution for analyzing US reports.
  • This method can standardize screening recommendations and treatment planning for hepatocellular carcinoma risk.
  • The approach facilitates AI-driven healthcare research by enabling large-scale text mining from clinical data.