未经评估:长时间漏气是胸外科手术最常见的并发症。术中渗漏部位检测是降低渗漏相关术后并发症风险的第一步。
UNASSIGNED:我们回顾性回顾了在我们机构接受肺切除术的患者的手术录像。在训练阶段,使用泄漏阳性内窥镜图像开发了基于深度学习的空气泄漏检测软件。在测试阶段,使用不同的数据集来评估我们针对每个预测框提出的应用.
UNASSIGNED:对从70个手术中获得的总共110个原始捕获和标记的图像进行了预处理,用于训练数据集。测试数据集包含64个泄漏阳性位点和45个泄漏阴性位点。测试数据集从93次手术中获得,包括58名存在漏气的患者和35名不存在漏气的患者。在测试阶段,我们的软件检测泄漏部位的灵敏度和特异性分别为81.3%和68.9%,分别。
UNASSIGNED:我们已经成功开发了基于深度学习的泄漏点检测应用程序,可用于放气的肺部。尽管当前版本仍然是具有有限训练数据集的原型,它是完全基于视觉信息的泄漏检测的新概念。
UNASSIGNED: Prolonged air leak is the most common complication of thoracic surgery. Intraoperative leak site detection is the first step in decreasing the risk of leak-related postoperative complications.
UNASSIGNED: We retrospectively reviewed the surgical videos of patients who underwent lung resection at our institution. In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. In the testing phase, a different data set was used to evaluate our proposed application for each predicted box.
UNASSIGNED: A total of 110 originally captured and labeled images obtained from 70 surgeries were preprocessed for the training data set. The testing data set contained 64 leak-positive and 45 leak-negative sites. The testing data set was obtained from 93 operations, including 58 patients in whom an air leak was present and 35 patients in whom an air leak was absent. In the testing phase, our software detected leak sites with a sensitivity and specificity of 81.3% and 68.9%, respectively.
UNASSIGNED: We have successfully developed a deep learning-based leak site detection application, which can be used in deflated lungs. Although the current version is still a prototype with a limited training data set, it is a novel concept of leak detection based entirely on visual information.