Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of Machine Learning

Abstract

An innovative algorithm to automatically assess blood perfusion quality of the intestinal sector in laparoscopic colorectal surgery is proposed. Traditionally, the uniformity of the brightness in indocyanine green-based fluorescence consists only in a qualitative, empirical evaluation, which heavily relies on the surgeon’s subjective assessment. As such, this leads to assessments that are strongly experience-dependent. To overcome this limitation, the proposed algorithm assesses the level and uniformity of indocyanine green used during laparoscopic surgery. The algorithm adopts a Feed Forward Neural Network receiving as input a feature vector based on the histogram of the green band of the input image. It is used to (i) acquire information related to perfusion during laparoscopic colorectal surgery, and (ii) support the surgeon in assessing objectively the outcome of the procedure. In particular, the algorithm provides an output that classifies the perfusion as adequate or inadequate. The algorithm was validated on videos captured during surgical procedures carried out at the University Hospital Federico II in Naples, Italy. The obtained results show a classification accuracy equal to $$99.9\%$$ 99.9 % , with a repeatability of $$1.9\%$$ 1.9 % . Finally, the real-time operation of the proposed algorithm was tested by analyzing the video streaming captured directly from an endoscope available in the OR.

Fluorescence-guided minimally-invasive resection of abdominal paragangliomas using indocyanine green

“…This pattern was detectable within 1–2 min after administration. This rapid vascular inflow is similar to that used in ICG-guided assessment of intestinal perfusion during abdominal procedures, which is one of the most commonly known indications for the use of ICG-FGS 47 – 50 . Tummers et al showed similar results in their case-report, which explored the intraoperative detection of paragangliomas using methylene blue (MB) 23 .…”

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

“…Additionally, they have been shown to predict the amount of bleeding in patients undergoing liver cancer resection [ 33 ]. Similarly, we can evaluate the quality of blood perfusion in laparoscopic colorectal surgery using machine learning [ 34 ]. As a clinically novel tool, artificial intelligence may improve our clinical decision-making process and guide the management of these patients, raising the quality of life for postoperative patients.…”

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