Department of Anesthesiology, Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 110, Heidelberg, 69120, Germany.
Department of Medical Informatics, Faculty of Informatics, Heilbronn University of Applied Sciences, Max-Planck-Str. 39, Heilbronn, 74081, Germany.
J Clin Monit Comput. 2021 Oct;35(5):1103-1109. doi: 10.1007/s10877-020-00568-x. Epub 2020 Aug 18.
Neurological surgery in the semi-sitting position is linked with a pronounced incidence of venous air embolism (VAE) which can be fatal and therefore requires continuous monitoring. Transesophageal echocardiography (TEE) provides a high sensitivity for the intraoperative detection of VAE; however, continuous monitoring with TEE requires constant vigilance by the anaesthesiologist, which cannot be ensured during the entire surgical procedure. We implemented a fully automatic VAE detection system for TEE based on a statistical model of the TEE images. In the sequence of images, the cyclic heart activity is regarded as a quasi-periodic process, and air bubbles are detected as statistical outliers. The VAE detection system was evaluated by means of receiver operating characteristic (ROC) curves using a data set consisting of 155.14 h of intraoperatively recorded TEE video and a manual classification of periods with visible VAE. Our automatic detection system accomplished an area under the curve (AUC) of 0.945 if all frames with visible VAE were considered as detection target, and an AUC of 0.990 if frames with the least severe optical grade of VAE were excluded from the analysis. Offline-review of the recorded TEE videos showed that short embolic events (≤ 2 min) may be overseen when monitoring TEE video manually. Automatic detection of VAE is feasible and could provide significant support to anaesthesiologists in clinical practice. Our proposed algorithm might possibly even offer a higher sensitivity compared to manual detection. The specificity, however, requires improvement to be acceptable for practical application. Trial Registration: German Clinical Trials Register (DRKS00011607).
半坐式神经外科手术与明显的静脉空气栓塞 (VAE) 发生率相关，可能导致致命后果，因此需要持续监测。经食管超声心动图 (TEE) 可提供术中 VAE 检测的高灵敏度；然而，TEE 的连续监测需要麻醉师持续警惕，而这在整个手术过程中无法得到保证。我们基于 TEE 图像的统计模型，为 TEE 开发了一种全自动 VAE 检测系统。在图像序列中，循环心脏活动被视为准周期过程，而气泡则被检测为统计异常值。使用包含 155.14 小时术中记录的 TEE 视频和手动分类的可见 VAE 期的数据集，通过接收者操作特征 (ROC) 曲线对 VAE 检测系统进行了评估。如果将所有可见 VAE 的帧都视为检测目标，我们的自动检测系统的曲线下面积 (AUC) 为 0.945，如果将 VAE 光学等级最低的帧排除在分析之外，则 AUC 为 0.990。对记录的 TEE 视频的离线审查表明，当手动监测 TEE 视频时，可能会忽略短暂的栓塞事件（≤2 分钟）。VAE 的自动检测是可行的，可以为临床实践中的麻醉师提供重要支持。与手动检测相比，我们提出的算法可能具有更高的灵敏度。然而，特异性仍需要改进，才能在实际应用中被接受。试验注册：德国临床试验注册处 (DRKS00011607)。
