PDF(Pubmed)
Abstract：
We developed a surgical support system that visualises important microanatomies using artificial intelligence (AI). This study evaluated its accuracy in recognising the thoracic nerves during lung cancer surgery. Recognition models were created with deep learning using images precisely annotated for nerves. Computational evaluation was performed using the Dice index and the Jaccard index. Four general thoracic surgeons evaluated the accuracy of nerve recognition. Further, the differences in time lag, image quality and smoothness of movement between the AI system and surgical monitor were assessed. Ratings were made using a five-point scale. The computational evaluation was relatively favourable, with a Dice index of 0.56 and a Jaccard index of 0.39. The AI system was used for 10 thoracoscopic surgeries for lung cancer. The accuracy of thoracic nerve recognition was satisfactory, with a recall score of 4.5 ± 0.4 and a precision score of 4.0 ± 0.9. Though smoothness of motion (3.2 ± 0.4) differed slightly, nearly no difference in time lag (4.9 ± 0.3) and image quality (4.6 ± 0.5) between the AI system and the surgical monitor were observed. In conclusion, the AI surgical support system has a satisfactory accuracy in recognising the thoracic nerves.
摘要：
我们开发了一个手术支持系统，该系统使用人工智能（AI）可视化重要的显微解剖结构。这项研究评估了其在肺癌手术中识别胸神经的准确性。识别模型是通过深度学习使用为神经精确注释的图像创建的。使用Dice指数和Jaccard指数进行计算评估。四名普通胸外科医师评估了神经识别的准确性。Further,时滞的差异，评估AI系统和手术监护仪之间的图像质量和运动平滑度.使用五点标度进行评级。计算评估相对较好，骰子指数为0.56，雅卡德指数为0.39。AI系统用于10例肺癌胸腔镜手术。胸神经识别的准确性令人满意，召回评分为4.5±0.4，精确度评分为4.0±0.9。虽然运动平稳性(3.2±0.4)略有差异,AI系统和手术监护仪之间的时间滞后（4.9±0.3）和图像质量（4.6±0.5）几乎没有差异。总之,AI手术支持系统在识别胸神经方面具有令人满意的准确性。
