Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers.

ABSTRACT:

Background

Humeral torsion is an important osseous adaptation in throwing athletes that can contribute to arm injuries. Currently there are no cheap and easy to use clinical tools to measure humeral torsion, inhibiting clinical assessment. Models with low error and "good" calibration slope may be helpful for prediction.

Hypothesis/purpose

To develop prediction models using a range of machine learning methods to predict humeral torsion in professional baseball pitchers and compare these models to a previously developed regression-based prediction model.

Study design

Prospective cohort.

Methods

An eleven-year professional baseball cohort was recruited from 2009-2019. Age, arm dominance, injury history, and continent of origin were collected as well as preseason s

SUBMITTER: Bullock GS 

PROVIDER: S-EPMC8975570 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature