Background: Fluorescence angiography has shown very promising results in reducing anastomotic leaks by allowing the surgeon to select optimally perfused tissue. However, subjective interpretation of the fluorescent signal still hinders broad application of the technique, as significant variation between different surgeons exists. Our aim is to develop an artificial intelligence algorithm to classify colonic tissue as 'perfused' or 'not perfused' based on intraoperative fluorescence angiography data. Methods: A classification model with a Resnet architecture was trained on a dataset of fluorescence angiography videos of colorectal resections at a tertiary referral centre. Frames corresponding to fluorescent and non-fluorescent segments of colon were used to train a classification algorithm. Validation using frames from patients not used in the training set was performed, including both data collected using the same equipment and data collected using a different camera. Performance metrics were calculated, and saliency maps used to further analyse the output. A decision boundary was identified based on the tissue classification. Results: A convolutional neural network was successfully trained on 1790 frames from 7 patients and validated in 24 frames from 14 patients. The accuracy on the training set was 100%, on the validation set was 80%. Recall and precision were respectively 100% and 100% on the training set and 68.8% and 91.7% on the validation set. Conclusion: Automated classification of intraoperative fluorescence angiography with a high degree of accuracy is possible and allows automated decision boundary identification. This will enable surgeons to standardise the technique of fluorescence angiography. A web based app was made available to deploy the algorithm.
翻译:背景:荧光血管血管造影显示在通过让外科医生选择最佳穿透组织来减少肛门泄漏方面非常有希望的结果。然而,对荧光信号的主观解释仍然阻碍着技术的广泛应用,因为不同外科医生之间存在显著的差异。我们的目标是开发人工智能算法,将科松组织分类为“透过”或“未透过”,其依据是手动的荧光血管血管血管造影数据。方法:一个带有Resnet结构的分类模型,在高校转中心对红外剖剖剖精度剖析剖析剖析剖析的数据集进行了培训。但是,对荧光和非荧光结结结部分进行了主观解释,从而无法广泛应用不同外科外科外科外科外科医生的分类算法。我们的目标是开发一个人工智能算法,将科组织内径组织分为“透析”和“未透析”分类。