Artificial intelligence assists surgeons’ decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection

Due to the difficult evaluation of the risk of anastomotic leakage (AL) after rectal cancer resection, the decision to perform a temporary ileostomy is not easily distinguishable. The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assi...

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Published in:European journal of surgical oncology 2023-02, Vol.49 (2), p.433-439
Main Authors: Shao, Shengli, Zhao, Yufeng, Lu, Qiyi, Liu, Lu, Mu, Lei, Qin, Jichao
Format: Article
Language:English
Subjects:
Anastomosis, Surgical
Anastomotic Leak - surgery
Anastomotic leakage
Artificial Intelligence
Bayes Theorem
Humans
Ileostomy
Rectal cancer
Rectal Neoplasms - surgery
Retrospective Studies
Surgeons
Temporary ileostomy
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container_title European journal of surgical oncology
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description Due to the difficult evaluation of the risk of anastomotic leakage (AL) after rectal cancer resection, the decision to perform a temporary ileostomy is not easily distinguishable. The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assist surgeons in the selective implementation of a temporary ileostomy. The data from 2240 patients with rectal cancer who received anterior resection were collected, and these patients were divided into one training and two test cohorts. Five AI algorithms, such as support vector machine (SVM), logistic regression (LR), Naive Bayes (NB), stochastic gradient descent (SGD) and random forest (RF) were employed to develop predictive models using clinical variables and were assessed using the two test cohorts. The SVM model indicated good discernment of AL, and might have increased the implementation of temporary ileostomy in patients with AL in the training cohort (p 
doi_str_mv 10.1016/j.ejso.2022.09.020
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The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assist surgeons in the selective implementation of a temporary ileostomy. The data from 2240 patients with rectal cancer who received anterior resection were collected, and these patients were divided into one training and two test cohorts. Five AI algorithms, such as support vector machine (SVM), logistic regression (LR), Naive Bayes (NB), stochastic gradient descent (SGD) and random forest (RF) were employed to develop predictive models using clinical variables and were assessed using the two test cohorts. The SVM model indicated good discernment of AL, and might have increased the implementation of temporary ileostomy in patients with AL in the training cohort (p &lt; 0.001). 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subjects Anastomosis, Surgical
Anastomotic Leak - surgery
Anastomotic leakage
Artificial Intelligence
Bayes Theorem
Humans
Ileostomy
Rectal cancer
Rectal Neoplasms - surgery
Retrospective Studies
Surgeons
Temporary ileostomy
title Artificial intelligence assists surgeons’ decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection
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