Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Abstract

Introduction: Thoracic aortic aneurysm diameter determination is paramount for the decision-making process regarding surgical management. Studies focusing in asymptomatic patients have determined prevalence of 0.16 to 0.36% of TAAs in imaging studies. Several groups have proposed automated aortic measurement tools as propaedeutic and therapeutic instruments. In this study we developed and tested an automatic 3-dimensional (3D) segmentation method for the thoracic aorta, applicable on computed tomography angiography (CTA) acquired using low-dose and standard dose protocol, with and without contrast enhancement; and to accurately calculate the 3D diameter information of the arterial segments. Methods: a retrospective cohort of all CT scans acquired in our service between 2016 and 2021 led to the selection of 587 CT exams including low and standard-dose radiation, with and without contrast enhancement. 527 exams were used for neural network training of an algorithm capable of aptly measuring the aortic diameters, using manual measurements performed by three medical specialists as a baseline. Sixty exams were used for validation. The algorithm was developed both for use with the support of PyRadiomics and for a self-made approach. Results: Aortic measurement using the algorithm supported by PyRadiomics resulted in mean absolute error values under 2mm. For the self-made approach, mean absolute error values were under 5mm. Conclusion: This study presents an effective automated solution for thoracic aortic measurement with good results in sets of standard or low-radiation exams, as well as those acquired with or without contrast enhancement; presenting a possibility for an auxiliary tool for automation of the process of measuring the diameter of the thoracic aorta.