Using deep learning to predict postoperative pain in reverse shoulder arthroplasty patients.

ABSTRACT:

Background

Most research on shoulder arthroplasty has predominantly concentrated on optimizing treatment to enhance shoulder function with comparatively less emphasis on postsurgical pain. Yet, pain is an equally significant or even more important outcome in orthopedic surgery. The aim of this study was to develop a deep learning algorithm for predicting postsurgical pain after reverse total shoulder arthroplasty (rTSA).

Methods

Clinical data of rTSA patients were extracted from a local shoulder arthroplasty registry and used to build an artificial neural network, which was set up with input from 34 preoperative features including demographics, disease-related information, clinical, and self-report assessments. The target variable was a binary classification derived from a n

SUBMITTER: Schneller T 

PROVIDER: S-EPMC12145030 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature