ArticleVeterinary surgery : VS2025
Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.
Article in Veterinary surgery : VS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Computed tomography versus magnetic resonance imaging for the perioperative management of acute thoracolumbar intervertebral disc extrusion in non-ambulatory chondrodystrophic dogs.Veterinary surgery : VS · 2026Article
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Development of an Explainable Machine Learning Computational Model for the Prediction of Severe Complications After Orchiectomy in Stallions.Animals : an open access journal from MDPI · 2026Article
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
- Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.Veterinary surgery : VS · 2025Article
- Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.Veterinary surgery : VS · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveTo develop and compare machine-learning algorithms to predict recovery of ambulation after decompressive surgery for acute intervertebral disc extrusion (IVDE). STUDY
designMulticenter retrospective cohort study. SAMPLE POPULATION: Deep-pain-negative dogs with acute IVDE (n = 162).
methodsClinical variables were preprocessed for machine learning and split into independent training and test sets in an 80:20 ratio. Each model was trained and internally validated on the full test set (Test
resultsRecovery of ambulation was recorded in 86/162 dogs (53.1%) in this sample population after decompressive surgery. The XGBoost algorithm achieved the best performance with an area under the receiver operating characteristic curve (AUC) of .9502 (95% CI: .8919-.9901), an accuracy of .8906 (95% CI: .8125-.9531), a sensitivity of .8750, and a specificity of .9063 on Test
conclusionMachine-learning algorithms may predict outcomes accurately in deep-pain-negative dogs with IVDE after decompressive surgery. The XGBoost algorithm performed best on tabular data from this veterinary population undergoing spinal surgery. CLINICAL SIGNIFICANCE: Machine-learning algorithms outperform current methods of prognostication. Pending external validation, machine-learning algorithms may be useful as assistive tools for surgical decision making.
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