Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis.

ABSTRACT:

Background

Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.

Aim

To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI).

Methods

We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spi

SUBMITTER: Cai ZH 

PROVIDER: S-EPMC9850934 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature