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Yu J, Taskén AA, Flade HM, Skogvoll E, Berg EAR, Grenne B, Rimehaug A, Kirkeby-Garstad I, Kiss G, Aakhus S. Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography. J Clin Monit Comput 2024; 38:281-291. [PMID: 38280975 DOI: 10.1007/s10877-023-01118-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/03/2023] [Indexed: 01/29/2024]
Abstract
We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients. In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations. In conclusion, autoMAPSE is feasible as an automatic tool for rapid and quantitative assessment of LV function, indicating its potential for hemodynamic monitoring.
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Affiliation(s)
- Jinyang Yu
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Anders Austlid Taskén
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hans Martin Flade
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Eirik Skogvoll
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Erik Andreas Rye Berg
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Bjørnar Grenne
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Audun Rimehaug
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Idar Kirkeby-Garstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Gabriel Kiss
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Svend Aakhus
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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Taskén AA, Berg EAR, Grenne B, Holte E, Dalen H, Stølen S, Lindseth F, Aakhus S, Kiss G. Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings. Artif Intell Med 2023; 144:102646. [PMID: 37783546 DOI: 10.1016/j.artmed.2023.102646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
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.
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Affiliation(s)
- Anders Austlid Taskén
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway.
| | - Erik Andreas Rye Berg
- 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.
| | - Bjørnar Grenne
- 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.
| | - Espen Holte
- 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.
| | - Håvard Dalen
- 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.
| | - Stian Stølen
- 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.
| | - Frank Lindseth
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway.
| | - Svend Aakhus
- 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.
| | - Gabriel Kiss
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7491 Trondheim, Norway.
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