Ivar M Salte1, Andreas Østvik2, Erik Smistad2, Daniela Melichova3, Thuy Mi Nguyen1, Sigve Karlsen4, Harald Brunvand4, Kristina H Haugaa5, Thor Edvardsen5, Lasse Lovstakken2, Bjørnar Grenne6. 1. Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Norway. 2. Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway. 3. Faculty of Medicine, University of Oslo, Norway; Department of Medicine, Hospital of Southern Norway, Arendal, Norway. 4. Department of Medicine, Hospital of Southern Norway, Arendal, Norway. 5. Faculty of Medicine, University of Oslo, Norway; Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway. 6. Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs Hospital, Trondheim, Norway. Electronic address: bjornar.grenne@ntnu.no.
Abstract
OBJECTIVES: This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application. BACKGROUND: GLS is an important parameter when evaluating left ventricular function. However, analyses of GLS are time consuming and demand expertise, and thus are underused in clinical practice. METHODS: In this study, 200 patients with a wide range of left ventricle (LV) function were included. Three standard apical cine-loops were analyzed using the AI pipeline. The AI method measured GLS and was compared with a commercially available semiautomatic speckle-tracking software (EchoPAC v202, GE Healthcare, Chicago, Illinois). RESULTS: The AI method succeeded to both correctly classify all 3 standard apical views and perform timing of cardiac events in 89% of patients. Furthermore, the method successfully performed automatic segmentation, motion estimates, and measurements of GLS in all examinations, across different cardiac pathologies and throughout the spectrum of LV function. GLS was -12.0 ± 4.1% for the AI method and -13.5 ± 5.3% for the reference method. Bias was -1.4 ± 0.3% (95% limits of agreement: 2.3 to -5.1), which is comparable with intervendor studies. The AI method eliminated measurement variability and a complete GLS analysis was processed within 15 s. CONCLUSIONS: Through the range of LV function this novel AI method succeeds, without any operator input, to automatically identify the 3 standard apical views, perform timing of cardiac events, trace the myocardium, perform motion estimation, and measure GLS. Fully automated measurements based on AI could facilitate the clinical implementation of GLS.
OBJECTIVES: This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application. BACKGROUND: GLS is an important parameter when evaluating left ventricular function. However, analyses of GLS are time consuming and demand expertise, and thus are underused in clinical practice. METHODS: In this study, 200 patients with a wide range of left ventricle (LV) function were included. Three standard apical cine-loops were analyzed using the AI pipeline. The AI method measured GLS and was compared with a commercially available semiautomatic speckle-tracking software (EchoPAC v202, GE Healthcare, Chicago, Illinois). RESULTS: The AI method succeeded to both correctly classify all 3 standard apical views and perform timing of cardiac events in 89% of patients. Furthermore, the method successfully performed automatic segmentation, motion estimates, and measurements of GLS in all examinations, across different cardiac pathologies and throughout the spectrum of LV function. GLS was -12.0 ± 4.1% for the AI method and -13.5 ± 5.3% for the reference method. Bias was -1.4 ± 0.3% (95% limits of agreement: 2.3 to -5.1), which is comparable with intervendor studies. The AI method eliminated measurement variability and a complete GLS analysis was processed within 15 s. CONCLUSIONS: Through the range of LV function this novel AI method succeeds, without any operator input, to automatically identify the 3 standard apical views, perform timing of cardiac events, trace the myocardium, perform motion estimation, and measure GLS. Fully automated measurements based on AI could facilitate the clinical implementation of GLS.
Authors: Amina Kunovac; Quincy A Hathaway; Emily N Burrage; Tyler Coblentz; Eric E Kelley; Partho P Sengupta; John M Hollander; Paul D Chantler Journal: Ultrasound Med Biol Date: 2022-08-04 Impact factor: 3.694
Authors: Patricia A Pellikka; Jordan B Strom; Gabriel M Pajares-Hurtado; Martin G Keane; Benjamin Khazan; Salima Qamruddin; Austin Tutor; Fahad Gul; Eric Peterson; Ritu Thamman; Shivani Watson; Deepa Mandale; Christopher G Scott; Tasneem Naqvi; Gary M Woodward; William Hawkes Journal: Front Cardiovasc Med Date: 2022-07-22