Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings.

Autor: Taskén AA; Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway. Electronic address: anders.a.tasken@ntnu.no., Berg EAR; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: erik.a.berg@ntnu.no., Grenne B; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: bjornar.grenne@ntnu.no., Holte E; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: espen.holte@ntnu.no., Dalen H; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway. Electronic address: havard.dalen@ntnu.no., Stølen S; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: stian.stolen@ntnu.no., Lindseth F; Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway. Electronic address: frankl@ntnu.no., Aakhus S; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: svend.aakhus@ntnu.no., Kiss G; Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway. Electronic address: gabriel.kiss@ntnu.no.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2023 Oct; Vol. 144, pp. 102646. Date of Electronic Publication: 2023 Aug 31.
DOI: 10.1016/j.artmed.2023.102646
Abstrakt: Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative evaluation of ultrasound imaging, which is a time-demanding and resource-intensive process with intraobserver- and interobserver variability. In practice, such measures can only be performed a limited number of times during the intervention. To overcome these difficulties, this study presents a robust method for automatic and quantitative monitoring of cardiac function based on 3D transesophageal echocardiography (TEE) B-mode ultrasound recordings of the left ventricle (LV). Such an assessment obtains consistent measurements and can produce a near real-time evaluation of ultrasound imagery. Hence, the presented method is time-saving and results in increased accessibility. The mitral annular plane systolic excursion (MAPSE), characterizing global LV function, is estimated by landmark detection and cardiac view classification of two-dimensional images extracted along the long-axis of the ultrasound volume. MAPSE estimation directly from 3D TEE recordings is beneficial since it removes the need for manual acquisition of cardiac views, hence decreasing the need for interference by physicians. Two convolutional neural networks (CNNs) were trained and tested on acquired ultrasound data of 107 patients, and MAPSE estimates were compared to clinically obtained references in a blinded study including 31 patients. The proposed method for automatic MAPSE estimation had low bias and low variability in comparison to clinical reference measures. The method accomplished a mean difference for MAPSE estimates of (-0.16±1.06) mm. Thus, the results did not show significant systematic errors. The obtained bias and variance of the method were comparable to inter-observer variability of clinically obtained MAPSE measures on 2D TTE echocardiography. The novel pipeline proposed in this study has the potential to enhance cardiac monitoring in perioperative- and intensive care settings.
Competing Interests: Declaration of competing interest
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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