深度学习提高超声心动图左心室应变的精度和可重复性：一项重测研究
Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study.
作者信息
Salte Ivar M, Østvik Andreas, Olaisen Sindre H, Karlsen Sigve, Dahlslett Thomas, Smistad Erik, Eriksen-Volnes Torfinn K, Brunvand Harald, Haugaa Kristina H, Edvardsen Thor, Dalen Håvard, Lovstakken Lasse, Grenne Bjørnar
机构信息
Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway.
出版信息
J Am Soc Echocardiogr. 2023 Jul;36(7):788-799. doi: 10.1016/j.echo.2023.02.017. Epub 2023 Mar 16.
AIMS
Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user-related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers and to compare the results to manual measurements.
METHODS
Two test-retest data sets (n = 40 and n = 32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader scenarios. Agreement, mean absolute difference, and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in 3 cardiac cycles was assessed by 2 readers and AI.
RESULTS
Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC = 3.7 vs 5.5, mean absolute difference = 1.4 vs 2.1, respectively; data set II: MDC = 3.9 vs 5.2, mean absolute difference = 1.6 vs 1.9, respectively; all P < .05). There was bias in GLS measurements in 13 of 24 test-retest interreader scenarios (largest bias, 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the 2 readers, respectively. Processing time for analyses of GLS by the AI method was 7.9 ± 2.8 seconds.
CONCLUSION
A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.
目的
超声心动图评估左心室（LV）功能受到测试-重测重复性一般的限制。一种基于深度学习的新型人工智能（AI）方法可对LV整体纵向应变（GLS）进行全自动测量，且可能通过减少与用户相关的变异性来提高超声心动图的临床应用价值。本研究的目的是评估在不同超声心动图检查者记录的重复超声心动图中，采用新型AI方法测量的LV GLS在患者内的测试-重测重复性，并将结果与手动测量结果进行比较。
方法
在不同中心获得两个测试-重测数据集（n = 40和n = 32）。每个中心由2名不同的超声心动图检查者立即连续采集重复记录。对于每个数据集，4名阅片者使用半自动方法在两次记录中测量GLS，构建测试-重测的阅片者间和阅片者内情况。将一致性、平均绝对差值和最小可检测变化（MDC）与AI分析结果进行比较。在10例患者的子集中，由两名阅片者和AI评估3个心动周期内的逐搏变异性。
结果
与阅片者间情况相比，AI的测试-重测变异性更低（数据集I：MDC = 3.7对5.5，平均绝对差值分别为1.4对2.1；数据集II：MDC = 3.9对5.2，平均绝对差值分别为1.6对1.9；所有P <.05）。在24个测试-重测阅片者间情况中，有13个GLS测量存在偏差（最大偏差为3.2应变单位）。相比之下，AI测量不存在偏差。AI和两名阅片者的逐搏MDC分别为1.5、2.1和2.3。AI方法分析GLS的处理时间为7.9 ± 2.8秒。
结论
一种用于自动测量LV GLS的快速AI方法降低了测试-重测变异性，并消除了两个测试-重测数据集中阅片者之间的偏差。通过提高精度和可重复性，AI可能会增加超声心动图的临床应用价值。
Zotero 插件