Focus Topic: Artificial Intelligence and EchocardiographyClinical InvestigationsDeep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study
Graphical abstract
Graphical abstract: Study design and main results: Two test-retest data sets were collected at 2 different centers (data set I and data set II). For both data sets, 2 different echocardiographers acquired separate recordings in immediate succession in each patient to create a test-retest pair of images (upper panel). For each patient, these test-retest recordings were analyzed by 4 readers who measured global longitudinal strain (GLS) using a commercially available semiautomatic method. This resulted in 12 interreader and 4 intrareader scenarios. In addition, GLS was measured using an artificial intelligence (AI) method based on deep learning. The test-retest measurement bias and minimal detectable change for all inter- and intrareader scenarios and the AI scenario are presented for both data sets (lower panels). Artificial intelligence–based measurements eliminated test-retest bias between readers and resulted in reduced test-retest variability.
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Drs. Salte and Østvik contributed equally to this work.
This work was supported by the Centre for Innovative Ultrasound Solutions (a Norwegian Research Council center for research-based innovation, project no. 237887), by the Norwegian Health Association, the Central Norway regional health authority, South-Eastern Norway regional health authority, the national program for clinical therapy research (project no. 2017207), and ProCardio Center for Innovation (no. 309762) from the Norwegian Research council. The HUNT Echocardiography study was funded by the Liaison Committee for Education, Research and Innovation in Central Norway and grants from the Simon Fougner Hartmann Family Fund, Denmark.
Conflicts of Interest: None.