首页|Affiliated Hospital of Inner Mongolia Medical University Reports Findings in Art ificial Intelligence (CT coronary fractional flow reserve based on artificial in telligence using different software: a repeatability study)
Affiliated Hospital of Inner Mongolia Medical University Reports Findings in Art ificial Intelligence (CT coronary fractional flow reserve based on artificial in telligence using different software: a repeatability study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According tonews reporting originating from Inner Mongolia, People's Republic of China, by NewsRx correspondents,research stated , "This study aims to assess the consistency of various CT-FFR software, to dete rmine thereliability of current CT-FFR software, and to measure relevant influe nce factors. The goal is to build asolid foundation of enhanced workflow and te chnical principles that will ultimately improve the accuracyof measurements of coronary blood flow reserve fractions."Our news editors obtained a quote from the research from the Affiliated Hospital of Inner MongoliaMedical University, "This improvement is critical for assessi ng the level of ischemia in patients with coronaryheart disease. 103 participan ts were chosen for a prospective research using coronary computed tomographyang iography (CCTA) assessment. Heart rate, heart rate variability, subjective pictu re quality, objectiveimage quality, vascular shifting length, and other factors were assessed. CT-FFR software including Ksoftware and S software are used for CT-FFR calculations. The consistency of the two software is assessedusing pair ed-sample t-tests and Bland-Altman plots. The error classification effect is use d to constructthe receiver operating characteristic curve. The CT-FFR measureme nts differed significantly between theK and S software, with a statistical sign ificance of P<0.05. In the Bland-Altman plot, 6% of the points(14 out of 216) fell outside the 95% consistency lev el. Single-factor analysis revealed that heart ratevariability, vascular disloc ation offset distance, subjective image quality, and lumen diameter significantl yinfluenced the discrepancies in CT-FFR measurements between two software progr ams (P <0.05). TheROC curve shows the highest AUC for the vessel shifting length, with an optimal cut-off of 0.85 mm.CT-FFR measurements vary among software from different manufacturers, leading to potential misclassification of qualitative diagnostics."
Inner MongoliaPeople's Republic of Chi naArtificial IntelligenceEmerging TechnologiesMachine LearningSoftware
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