Matthew Brown1, Patrick Browning2, M Wasil Wahi-Anwar3, Mitchell Murphy3, Jayson Delgado2, Hayit Greenspan4, Fereidoun Abtin5, Shahnaz Ghahremani5, Nazanin Yaghmai5, Irene da Costa3, Moshe Becker2, Jonathan Goldin3. 1. Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024; Department of Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024. Electronic address: mbrown@mednet.ucla.edu. 2. RADLogics, Piedmont, California. 3. Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024; Department of Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024. 4. RADLogics, Piedmont, California; Department of Bio-Medical Engineering, Tel-Aviv University, Tel Aviv-Yafo, Israel. 5. Department of Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd., Suite 650, Los Angeles, CA 90024.
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.
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.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Andreas M Rauschecker; Jeffrey D Rudie; Long Xie; Jiancong Wang; Michael Tran Duong; Emmanuel J Botzolakis; Asha M Kovalovich; John Egan; Tessa C Cook; R Nick Bryan; Ilya M Nasrallah; Suyash Mohan; James C Gee Journal: Radiology Date: 2020-04-07 Impact factor: 11.105