Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases.
Sindre Olaisen, Erik Smistad, Torvald Espeland, Jieyu Hu, David Pasdeloup, Andreas Østvik, Svend Aakhus, Assami Rösner, Siri Malm, Michael Stylidis, Espen Holte, Bjørnar Grenne, Lasse Løvstakken, Havard Dalen
Author Information
Sindre Olaisen: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
Erik Smistad: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
Torvald Espeland: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
Jieyu Hu: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway.
David Pasdeloup: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
Andreas Østvik: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway.
Svend Aakhus: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway.
Assami Rösner: Department of Cardiology, University Hospital of North Norway, Tromsø, Norway.
Siri Malm: Institute for Clinical Medicine, UiT, The Arctic University of Norway, Tromsø, Norway.
Michael Stylidis: Department of Cardiology, University Hospital of North Norway, Tromsø, Norway.
Espen Holte: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
Bjørnar Grenne: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway.
Lasse Løvstakken: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway.
Havard Dalen: Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Prinsesse Kristinas Gate 3, 7030 Trondheim, Norway. ORCID
AIMS: Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test-retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases. METHODS AND RESULTS: Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P < 0.001) compared with standard clinical workflow. Test-retest reproducibility of AI measurements was superior in inter-observer scenarios and non-inferior in intra-observer scenarios. AI measurements showed good agreement with reference measurements both in real time and in large research databases. CONCLUSION: The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts.
