■超声心动图在心血管医学中对筛查至关重要,诊断,和监测。人工智能(AI)具有通过减少变异性和分析时间来改善超声心动图的潜力。虽然3D超声心动图变得越来越准确,2D成像仍然主导着临床护理。我们旨在评估人类读者之间左心室(LV)容积和功能测量的一致性,完全自动化的AI2D算法,还有3D心脏模型.
■对109例接受2D和3D经胸超声心动图检查的患者进行了回顾性分析。左心室舒张末期和收缩末期容积(LVEDV,LVESV)和射血分数(LVEF)由两名操作者测量,一种商用人工智能算法(US2ai),还有3D心脏模型.通过集成的半自动软件和AI算法测量全局纵向应变(GLS)。结果包括协议[偏见,协议限制和皮尔逊相关性(R)]。
■对于左心室容量测量,AI算法与人类操作员的平均值密切相关(LVEDV的r=0.89,LVESV的r=0.92),高于运算符之间的值(分别为r=0.74和r=0.84,p<0.01)。对于LVEDV的可靠性测量也看到了同样的趋势,但不是LVESV。AI在测量LVEF方面表现出与人类操作员相当的性能,而与人类操作员和AI测量相比,3D心脏模型的相关性和可靠性较弱。GLS的人类操作员与AI之间的相关性仅为中等。
■这项研究表明,基于AI的超声心动图是在临床实践中准确评估LV体积和LVEF的有前途的工具。基于人工智能的措施显示出显著较低的操作员间变异性,从而提高这些评估的一致性和可靠性。此外,人工智能可能被证明对进行回顾性批量分析特别有效,为全面评估过去的数据提供了一个有价值的工具。
UNASSIGNED: Echocardiography is essential in cardiovascular medicine for screening, diagnosis, and monitoring. Artificial intelligence (AI) has the potential to improve echocardiography by reducing variability and analysis time. While 3D echocardiography is becoming more accurate, 2D imaging still dominates clinical care. We aimed to evaluate agreement in measures of left ventricular (LV) volumes and function between human readers, a fully automated AI 2D algorithm, and the 3D Heart Model.
UNASSIGNED: A retrospective analysis was conducted on 109 patients who underwent 2D and 3D transthoracic echocardiography. LV end-diastolic and end-systolic volumes (LVEDV, LVESV) and ejection fraction (LVEF) were measured by two operators, a commercially available AI algorithm (US2ai), and the 3D Heart Model. Global longitudinal strain (GLS) was measured by the integrated semi-automated software and the AI algorithm. Outcomes included measures of agreement [bias, limit of agreement and Pearson\'s correlation (R)].
UNASSIGNED: For LV volume measurements, the AI algorithm was strongly correlated with the average of the human operators (r = 0.89 for LVEDV and r = 0.92 for LVESV), which was higher than between the operators (r = 0.74 and r = 0.84, respectively, p < 0.01). The same trend was seen for measures of reliability with respect to LVEDV, but not LVESV. AI demonstrated comparable performance to human operators in measuring LVEF, while the 3D Heart Model had a weaker correlation and reliability compared with human operators and AI measurements. The correlation between human operators and AI for GLS was only moderate.
UNASSIGNED: This study demonstrates AI-based echocardiography as a promising tool for accurately assessing LV volumes and LVEF in clinical practice. AI-based measures demonstrated a significantly lower inter-operator variability, thereby improving the consistency and reliability of these assessments. Moreover, AI may prove particularly effective for conducting retrospective bulk analyses, offering a valuable tool for comprehensive evaluations of past data.