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Fluorescence angiography classification in colorectal surgery -- A preliminary report by Soares, Antonio S; Bano, Sophia; Clancy, Neil T; Lovat, Laurence B; Stoyanov, Danail; Chand, Manish is a scholarly article available to read on EtoBox.

What is Fluorescence angiography classification in colorectal surgery -- A preliminary report about?

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 successfull

Author

Soares, Antonio S; Bano, Sophia; Clancy, Neil T; Lovat, Laurence B; Stoyanov, Danail; Chand, Manish

Published

2022

Language

EN