Integration of Chest CT CAD into the Clinical Workflow and Impact on Radiologist Efficiency

Matthew Brown*, Patrick Browning, M. Wasil Wahi-Anwar, Mitchell Murphy, Jayson Delgado, Hayit Greenspan, Fereidoun Abtin, Shahnaz Ghahremani, Nazanin Yaghmai, Irene da Costa, Moshe Becker, Jonathan Goldin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale and Objectives: The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow. Materials and Methods: A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study. Results: CAD assistance reduced reading times by 7%–44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report. Conclusion: This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.

Original languageEnglish
Pages (from-to)626-631
Number of pages6
JournalAcademic Radiology
Volume26
Issue number5
DOIs
StatePublished - May 2019

Funding

FundersFunder number
RADLogics, Inc.
Tobacco-Related Disease Research Program26IR-0020
Tobacco-Related Disease Research Program

    Keywords

    • Computer-aided detection
    • Lung nodules

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