Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA.
Department of Cardiovascular Medicine, Houston Methodist DeBakey Heart Centre, Houston, TX, USA.
Nat Commun. 2021 Aug 31;12(1):5192. doi: 10.1038/s41467-021-25503-9.
Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.
尽管几十年来取得了不断的进步，但超声心动图中丰富的时间分辨数据仍未得到充分利用。人类评估将心脏壁运动的复杂模式简化为一小部分心脏功能的测量值。所有现代超声心动图人工智能 (AI) 系统在设计上都受到类似的限制——自动化测量相同的简化指标，而不是利用嵌入式丰富数据。在临床决策受疾病严重程度的主观评估指导的情况下，这种未充分利用的情况最为明显。预测机械循环支持情况下术后右心室衰竭 (RV 衰竭) 的可能性就是一个这样的例子。在这里，我们描述了一个经过训练的视频人工智能系统，该系统使用术前超声心动图中完整的时空信息密度来预测术后 RV 衰竭。我们实现了 0.729 的 AUC，并表明该机器学习系统在独立评估中显著优于同一任务的人类专家团队。
