Cho Hyun-Hae, Lee So Mi, You Sun Kyoung
Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea.
Department of Radiology, School of Medicine, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea.
Pediatr Cardiol. 2024 Aug 31. doi: 10.1007/s00246-024-03630-6.
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.
从先天性心脏病患者的心脏CT扫描中获得的容积数据对于确定患者状况和做出适当管理决策非常重要。本研究的目的是评估左心室（LV）、右心室（RV）或功能性单心室（FSV）容积的观察者内、观察者间和研究间的可重复性。并比较手动测量和使用半自动分割工具测量之间的差异。2020年1月至2022年12月，共有127例患者（56例女性，71例男性；平均年龄82.1个月）接受了儿科心脏CT检查。容积数据包括收缩末期和舒张末期容积以及计算得出的射血分数，这些数据由三位读者分别通过传统的半自动区域生长算法（CM，TeraRecon，TeraRecon公司，美国加利福尼亚州圣马特奥）和基于深度学习的标注程序（DLS，Medilabel，Ingradient公司，韩国首尔）获取，这三位读者之前在放射学或图像提取方面具有不同的背景知识或经验。使用观察者内和观察者间的一致性来比较可重复性。并使用重建时间和在重建时间减少到不到5分钟之前重新配置的测试次数来衡量可用性。在所有分析器中，观察者间和观察者内的一致性在DLS中比在CM中表现出更好的一致性程度。与CM相比，DLS用于重建的时间明显更短。并且与CM相比，DLS在重新配置之前需要的测试次数明显更少。基于深度学习的标注程序对于先天性心脏病患者容积数据的测量可能是一种更准确的方法，其可重复性比传统方法更好。
