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Evaluation of Coronary Artery Disease and Coronary Anomalies with a Handheld Smartphone

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Abstract

The purpose of this study was to determine the diagnostic accuracy of an iPhone for evaluation of the coronary arteries on coronary CT angiography (CTA) in comparison to a standard clinical workstation. Fifty coronary CTA exams were selected to include a range of normal and abnormal cases including both coronary artery disease (CAD) of varying severity and coronary artery anomalies. Two cardiac radiologists reviewed each exam on a standard clinical workstation initially and then on an iPhone 6 after a washout period. Coronary stenosis was evaluated on a 4-point scale and presence of coronary anomalies was recorded. Two additional cardiac radiologists reviewed all cases in consensus on the standard workstation and these results were used as the reference standard. When reader results were compared to the reference standard, there was no significant difference in agreement for per-vessel stenosis scores using either the iPhone or standard clinical workstation. The intraobserver intertechnology agreement on a per-vessel basis for obstructive CAD were 97.4% (299/307, kappa = 0.777) and 97.5% (317/325, kappa = 0.804) for the two readers. All cases of coronary anomalies were identified by both readers regardless of the device used. Coronary CTA examinations can be interpreted on a smartphone with diagnostic accuracy comparable to a standard workstation. 3D visualization app on the iPhone may facilitate urgent coronary CTA review when a workstation is not available.

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Correspondence to Cheng Ting Lin.

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Conflict of Interest

Dr. Elliot K. Fishman discloses the following relationships: Siemens Medical Systems, research grant support, and HIP Graphics, co-founder. The other authors have no competing interests or disclosures to declare.

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This research was approved by the institutional review board.

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Lin, C.T., Zimmerman, S.L., Chu, L.C. et al. Evaluation of Coronary Artery Disease and Coronary Anomalies with a Handheld Smartphone. J Digit Imaging 30, 732–737 (2017). https://doi.org/10.1007/s10278-017-9982-5

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  • DOI: https://doi.org/10.1007/s10278-017-9982-5

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