Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

ABSTRACT:

Purpose

Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.

Methods

This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (7

SUBMITTER: Lippenberger F 

PROVIDER: S-EPMC10811180 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature