Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors

• Published in: Diagnostics (Basel) Vol. 12; no. 8; p. 1987
• Main Authors: De Santis, Domenico, Tremamunno, Giuseppe, Rucci, Carlotta, Polidori, Tiziano, Zerunian, Marta, Piccinni, Giulia, Pugliese, Luca, Masci, Benedetta, Ubaldi, Nicolò, Laghi, Andrea, Caruso, Damiano
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-08-2022; MDPI
• Subjects: Analysis; Angina pectoris; Angiography; automated coronary analysis; Automation; Cardiovascular disease; Care and treatment; CCTA; coronary arteries; coronary artery disease; Coronary heart disease; Coronary vessels; CT imaging; Diagnosis; Heart rate; Identification; Iodine; Medical personnel; Patients; Pulmonary arteries; Software packages; Tomography; Training; Work stations; Italy; United States > US; coronary artery disease; CCTA; coronary arteries; automated coronary analysis
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12081987

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.