Automated computer-aided stenosis detection at coronary CT angiography: initial experience.
Objective: To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).Methods: We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were anal...
| Publicado en: | European Radiology Vol. 20; no. 5; pp. 1160 - 1168 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2010
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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=105167267&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105167267 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2010 vid: 20 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105167267 49024266 NLM19890640 2010618426 10.1007/s00330-009-1644-7 NLM19890640 105167267 ppf: 1160 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated computer-aided stenosis detection at coronary CT angiography: initial experience. aug: au: Arnoldi E Gebregziabher M Schoepf UJ Goldenberg R Ramos-Duran L Zwerner PL Nikolaou K Reiser MF Costello P Thilo C Arnoldi, Elisabeth Gebregziabher, Mulugeta Schoepf, U Joseph Goldenberg, Roman Ramos-Duran, Luis Zwerner, Peter L Nikolaou, Konstantin Reiser, Maximilian F Costello, Philip Thilo, Christian affil: Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, MSC 226, Charleston, SC 29401, USA sug: subj: Coronary Angiography Methods Coronary Stenosis Radiography Information Science Methods Radiographic Image Interpretation, Computer-Assisted Methods Tomography, X-Ray Computed Methods Algorithms Female Human Logistic Regression Male Middle Age Sensitivity and Specificity Software Middle Aged: 45-64 years Female Male ab: Objective: To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).Methods: We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.Results: QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.Conclusion: Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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