Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.
The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital...
| Published in: | Journal of Digital Imaging Vol. 32; no. 4; pp. 618 - 625 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137642029&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642029 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2019 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642029 137642029 137642029 10.1007/s10278-018-0168-6 137642029 ppf: 618 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD. aug: au: Mayo, Ray Cody Kent, Daniel Sen, Lauren Chang Kapoor, Megha Leung, Jessica W. T. Watanabe, Alyssa T. affil: Department of Diagnostic Imaging, Breast Imaging Division, MD Anderson Center, University of Texas, 1515 Holcombe Blvd., 77030, Houston, TX, USA sug: subj: Mammography False Positive Results Diagnostic Errors Prevention and Control Artificial Intelligence Utilization Image Interpretation, Computer Assisted Methods Human Retrospective Design Comparative Studies Sensitivity and Specificity Cancer Screening Methods Descriptive Statistics Confidence Intervals Calcinosis Diagnosis, Computer Assisted ab: The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital mammograms between January 1, 2013, and March 31, 2013, and the number of marked regions of interest of two different systems was compared for sensitivity and specificity in cancer detection. The count of false-positive marks per image (FPPI) of the two systems was also evaluated as well as the number of cases that were completely mark-free. All results showed statistically significant reductions in false marks with the use of AI-CAD vs CAD (confidence interval = 95%) with no reduction in sensitivity. There is an overall 69% reduction in FPPI using the AI-based CAD as compared to CAD, consisting of 83% reduction in FPPI for calcifications and 56% reduction for masses. Almost half (48%) of cases showed no AI-CAD markings while only 17% show no conventional CAD marks. There was a significant reduction in FPPI with AI-CAD as compared to CAD for both masses and calcifications at all tissue densities. A 69% decrease in FPPI could result in a 17% decrease in radiologist reading time per case based on prior literature of CAD reading times. Additionally, decreasing false-positive recalls in screening mammography has many direct social and economic benefits. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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