Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.

ABSTRACT:

Objective

To develop a computed tomography (CT)-radiomics-based machine-learning algorithm for prediction of functional recovery in paraplegic dogs with acute intervertebral disc extrusion (IVDE).

Study design

Multivariable prediction model development.

Sample population

Paraplegic dogs with acute IVDE: 128 deep-pain positive and 86 deep-pain negative (DPN).

Methods

Radiomics features from noncontrast CT were combined with deep-pain perception in an extreme gradient algorithm using an 80:20 train-test split. Model performance was assessed on the independent test set (Test_full) and on the test set of DPN dogs (Test_DPN). Deep-pain perception alone served as the control.

Results

Recovery of ambulation was recorded in 165/214 dogs (77.1

SUBMITTER: Low D 

PROVIDER: S-EPMC12528818 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature