A novel system applying artificial intelligence in the identification of air leak sites.

Objective: 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. Methods: 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. Results: 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. Conclusions: 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.