Computer-aided stenosis detection at coronary CT angiography: effect on performance of readers with different experience levels.
Objectives: To evaluate the effect of a computer-aided detection (CAD) algorithm for coronary CT angiography (cCTA) on the performance of readers with different experience levels.Methods: We studied 50 patients (18 women, 58 ± 11 years) who had undergone cCTA and quantitative coronary angiography (Q...
| Publicado en: | European Radiology Vol. 25; no. 3; pp. 694 - 703 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109776637&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109776637 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Mar2015 vid: 25 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109776637 NLM25316059 2012906465 10.1007/s00330-014-3460-y NLM25316059 109776637 ppf: 694 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Computer-aided stenosis detection at coronary CT angiography: effect on performance of readers with different experience levels. aug: au: Thilo, Christian Gebregziabher, Mulugeta Meinel, Felix G Goldenberg, Roman Nance Jr, John W Arnoldi, Elisabeth M Soma, Lashonda D Ebersberger, Ullrich Blanke, Philip Coursey, Richard L Rosenblum, Michael A Zwerner, Peter L Schoepf, U Joseph Nance, John W Jr affil: Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, MSC 226, Charleston, SC, 29401, USA. sug: subj: Coronary Angiography Methods Coronary Stenosis Radiography Tomography, X-Ray Computed Methods Aged Algorithms Clinical Competence Standards Contrast Media Diagnostic Use Female Human Image Processing, Computer Assisted Methods Male Middle Age Sensitivity and Specificity Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Objectives: To evaluate the effect of a computer-aided detection (CAD) algorithm for coronary CT angiography (cCTA) on the performance of readers with different experience levels.Methods: We studied 50 patients (18 women, 58 ± 11 years) who had undergone cCTA and quantitative coronary angiography (QCA). Eight observers with varying experience levels evaluated all studies for ≥50 % coronary artery stenosis. After 3 months, the same observers re-evaluated all studies, this time guided by a CAD system. Their performance with and without the CAD system (sensitivity, specificity, positive predictive value and negative predictive value) was assessed using the Likelihood Ratio Χ(2) test both at the per-patient and per-vessel levels.Results: The sensitivity of the CAD system alone for stenosis detection was 71 % per-vessel and 100 % per-patient. There were 54 false positive (FP) findings within 199 analyzed vessels, most of them associated with non-obstructive (<50 %) lesions. With CAD, one (out of three, 33 %) inexperienced reader's per-patient sensitivity and negative predictive value significantly improved from 79 % to 100 % (P = 0.046) and from 90 % to 100 % (P = 0.034), respectively. Other readers' performance indices showed no statistically significant change.Conclusions: Our results suggest that CAD can improve some inexperienced readers' sensitivity for diagnosing coronary artery stenosis at cCTA. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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