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AI Accelerated Human-in-the-loop Structuring of Radiology Reports

AMIA ... Annual Symposium proceedings, 2020, Vol.2020, p.1305-1314 [Peer Reviewed Journal]

2020 AMIA - All rights reserved. ;2020 AMIA - All rights reserved. 2020 ;EISSN: 1942-597X ;PMID: 33936507

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  • Title:
    AI Accelerated Human-in-the-loop Structuring of Radiology Reports
  • Author: Wu, Joy T ; Syed, Ali ; Ahmad, Hassan ; Pillai, Anup ; Gur, Yaniv ; Jadhav, Ashutosh ; Gruhl, Daniel ; Kato, Linda ; Moradi, Mehdi ; Syeda-Mahmood, Tanveer
  • Subjects: Databases, Factual ; Humans ; Natural Language Processing ; Radiology ; Research Report
  • Is Part Of: AMIA ... Annual Symposium proceedings, 2020, Vol.2020, p.1305-1314
  • Description: Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image. AI techniques can accelerate the concept expansion and phrasal grouping tasks to efficiently create a domain specific lexicon ontology for structuring reports. Using Chest X-ray (CXR) reports as an example, we demonstrate that with robust vocabulary, even a simple NLP pipeline can extract 83 directly mentioned abnormalities (Ave. recall=93.83%, precision=94.87%) and 47 abnormality/normality descriptions of key anatomies. The richer vocabulary enables identification of additional label mentions in 10 out of 13 labels (compared to baseline methods). Furthermore, it captures expert insight into critical differences between observed and inferred descriptions, and image quality issues in reports. Finally, we show how the CXR ontology can be used to anatomically structure labeled output.
  • Publisher: United States: American Medical Informatics Association
  • Language: English
  • Identifier: EISSN: 1942-597X
    PMID: 33936507
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
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