Improving Reproducibility of Volumetric Evaluation Using Computed Tomography in Pediatric Patients with Congenital Heart Disease.

The volumetric data obtained from the cardiac CT scan of congenital heart disease patients is important for defining patient's status and making decision for proper management. The objective of this study is to evaluate the intra-observer, inter-observer, and interstudy reproducibility of left ventricular (LV) and right ventricular (RV) or functional single-ventricle (FSV) volume. And compared those between manual and using semi-automated segmentation tool. Total of 127 patients (56 female, 71 male; mean age 82.1 months) underwent pediatric protocol cardiac CT from January 2020 to December 2022. The volumetric data including both end-systolic and -diastolic volume and calculated EF were derived from both conventional semiautomatic region growing algorithms (CM, TeraRecon, TeraRecon, Inc., San Mateo, CA, USA) and deep learning-based annotation program (DLS, Medilabel, Ingradient, Inc., Seoul, Republic of Korea) by three readers, who have different background knowledge or experience of radiology or image extraction before. The reproducibility was compared using intra- and inter-observer agreements. And the usability was measured using time for reconstruction and number of tests that were reconfigured before the reconfiguration time was reduced to less than 5 min. Inter- and intra-observer agreements showed better agreements degrees in DLS than CM in all analyzers. The time used for reconstruction showed significantly shorter in DLS compared with CM. And significantly small numbers of tests before the reconfiguration is needed in DLS than CM. Deep learning-based annotation program can be more accurate way for measurement of volumetric data for congenital heart disease patients with better reproducibility than conventional method.