Surface Models of the Four Chambers in Young Adult Hearts with Average Volumes Measured by Artificial Intelligence Tools

Abstract

The average volumes of normal heart chambers in computed tomography (CT) are used not only as clinical criterions for heart disease diagnosis, but also as references in cardiology. With the development of artificial intelligence (AI), numerous CT data can be analyzed and segmented automatically. This study aimed to determine the average volumes of the four chambers in healthy adult hearts and present surface models with the average volume. Coronary CT angiographs of 508 Korean individuals (330 men and 178 women, 20 – 39 years old) were obtained. An automatic segmentation module for 3D Slicer was developed using machine learning in Anatomage KoreaTM. Using the module, the four chambers and heart valves in the CT were segmented and reconstructed into surface models. Surface models of the four chambers of identical hearts in the CT were produced using SimplewareTM. The volumes of structures were measured using Sim4life Light and statistically analyzed. After determining the average volumes of the four chambers, surface models of the average volumes were constructed. In both software measurements, the atrial volumes of females increased with age, and the ventricular volumes of males decreased significantly with age. The atrial and ventricular volumes of Simpleware were larger and smaller than those of Anatomage, respectively, because of errors in the Simpleware. Regarding the volume measurement, our module developed in this study was more accurate than the Simpleware. The average volume and three-dimensional models used in this study can be used not only for clinical purposes, but also for educational or industrial purposes.