Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany.
Gut. 2022 Dec;71(12):2388-2390. doi: 10.1136/gutjnl-2021-326470. Epub 2022 Sep 15.
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.
在这项研究中，我们旨在开发一种人工智能临床决策支持解决方案，以减轻在复杂的内窥镜手术（例如内窥镜黏膜下剥离术和经口内镜肌切开术）期间操作员依赖性的限制，例如出血和穿孔。基于 DeepLabv3 的模型被训练来描绘来自这些手术的内窥镜静止图像中的血管、组织结构和器械。平均交叉验证的交并比和骰子分数分别为 63%和 76%。应用于第三空间内窥镜手术的标准化视频剪辑，该算法显示出 85%的平均血管检测率和 0.75/min 的假阳性率。这些性能统计数据表明，该算法在手术安全性、时间和培训方面具有潜在的临床益处。
