Using machine and deep learning to predict short-term complications following trigger digit release surgery.

ABSTRACT:

Background

Trigger finger is a common disorder of the hand characterized by pain and locking of the digits during flexion or extension. In cases refractory to nonoperative management, surgical release of the A1 pulley can be performed. This study evaluates the ability of machine learning (ML) techniques to predict short-term complications following trigger digit release surgery.

Methods

A retrospective study was conducted using data for trigger digit release from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005-2020. Outcomes of interest were 30-day complications and 30-day return to the operating room. Three ML algorithms were evaluated - a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tr

SUBMITTER: Shah RM 

PROVIDER: S-EPMC11770221 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature