| Autor: |
Chernina VY; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow., Pisov ME; Skolkovo Institute of Science and Technology, Moscow., Belyaev MG; Skolkovo Institute of Science and Technology, Moscow., Bekk IV; National Medical and Surgical Center named after N.I. Pirogov of the Ministry of Healthcare of the Russian Federation, Moscow., Zamyatina KA; A.V. Vishnevsky National Medical Research Center of Surgery, Moscow., Korb TA; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow., Aleshina OO; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow., Shukina EA; Moscow State University of Medicine and Dentistry named after A.I. Evdokimov, Moscow., Solovev AV; Sklifosovsky Clinical and Research Institute for Emergency Medicine, Moscow., Skvortsov RA; National Medical and Surgical Center named after N.I. Pirogov of the Ministry of Healthcare of the Russian Federation, Moscow., Filatova DA; Lomonosov Moscow State University, Moscow., Sitdikov DI; The First Sechenov Moscow State Medical University, Moscow., Chesnokova AO; The First Sechenov Moscow State Medical University, Moscow., Morozov SP; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow., Gombolevsky VA; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow. |
| Abstrakt: |
Aim To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed tomography (CT) of chest organs.Material and methods This analytical, retrospective, transversal study randomly included 100 patients from a database of a united radiological informational service (URIS). The patients underwent LDCT as a part of the project "Low-dose chest computed tomography as a screening method for detection of lung cancer and other diseases of chest organs" (n=50) and chest CT according to a standard protocol (n=50) in outpatient clinics of Moscow. Each image was read by two radiologists on a Syngo. via VB20 workstation. In addition, each image was evaluated with a developed machine-learning algorithm, which provides a completely automatic measurement of EAT.Results Comparison of EAT volumes obtained with chest LDCT and CT showed highly consistent results both for the expert-performed semi-automatic analyses (correlation coefficient >98 %) and between the expert layout and the machine-learning algorithm (correlation coefficient >95 %). Time of performing segmentation and volumetry on one image with the machine-learning algorithm was not longer than 40 sec, which was 30 times faster than the quantitative analysis performed by an expert and potentially facilitated quantification of the EAT volume in the clinical conditions.Conclusion The proposed method of automatic volumetry will expedite the analysis of EAT for predicting the risk of ischemic heart disease. |
