Sabo Sigbjorn, Pettersen Håkon, Bøen Gunn C, Jakobsen Even O, Langøy Per K, Nilsen Hans O, Pasdeloup David, Smistad Erik, Østvik Andreas, Løvstakken Lasse, Stølen Stian, Grenne Bjørnar, Dalen Håvard, Holte Espen
Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, PO Box 8905, Trondheim 7491, Norway.
Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
Eur Heart J Imaging Methods Pract. 2025 Jul 21;3(2):qyaf094. doi: 10.1093/ehjimp/qyaf094. eCollection 2025 Jul.
The low reproducibility of echocardiographic measurements challenges the identification of subtle changes in left ventricular (LV) function. Deep learning (DL) methods enable real-time analysis of acquisitions and may improve echocardiography. The aim of this study was to evaluate the impact of DL-based guidance and automated measurements on the reproducibility of LV global longitudinal strain (GLS), end-diastolic (EDV) and end-systolic (ESV) volume, and ejection fraction (EF).
Forty-six patients (24 breast cancer and 22 general cardiology patients) were included and underwent four consecutive echocardiograms. Six were included twice, totalling 52 inclusions and 208 echocardiograms. One sonographer-cardiologist pair used DL guidance and measurements (DL group), while another did not use DL tools and performed manual measurements (manual group). DL group recordings were also measured using a commercially available DL-based EF tool. For GLS, the DL group had a 30% lower test-retest variability than the manual group (minimal detectable change 2.0 vs. 2.9, = 0.036). LV volumes had ∼40% lower minimal detectable changes in the DL group vs. the manual group (32 mL vs. 52 mL for EDV and 18 mL vs. 32 mL for ESV, ≤ 0.006). This did not translate to a significant improvement in EF reproducibility in the DL group. The benchmarking method showed similar results compared with the manual group.
Combining real-time DL guidance with automated measurements improved the reproducibility of LV size and function measurements compared with usual care, but future studies are needed to evaluate its clinical effect.
NCT06310330.
超声心动图测量的低重复性对识别左心室（LV）功能的细微变化提出了挑战。深度学习（DL）方法能够对采集的数据进行实时分析，并可能改善超声心动图检查。本研究的目的是评估基于DL的指导和自动测量对LV整体纵向应变（GLS）、舒张末期（EDV）和收缩末期（ESV）容积以及射血分数（EF）重复性的影响。
纳入46例患者（24例乳腺癌患者和22例普通心脏病患者），并连续进行4次超声心动图检查。其中6例患者被纳入两次，共计52次纳入和208次超声心动图检查。一组超声医师 - 心脏病专家使用DL指导和测量（DL组），而另一组未使用DL工具，进行手动测量（手动组）。DL组的记录也使用市售的基于DL的EF工具进行测量。对于GLS，DL组的重测变异性比手动组低30%（最小可检测变化为2.0对2.9，P = 0.036）。与手动组相比，DL组LV容积的最小可检测变化降低了约40%（EDV为32 mL对52 mL，ESV为18 mL对32 mL，P≤0.006）。这并未转化为DL组EF重复性的显著改善。与手动组相比，基准测试方法显示了相似的结果。
与常规护理相比，将实时DL指导与自动测量相结合可提高LV大小和功能测量的重复性，但需要未来的研究来评估其临床效果。
NCT06310330。
