Peer-reviewed veterinary case report
Predicting recovery after spinal surgery in paraplegic dogs
By Low, Daniel & Rutherford, Scott·Published in Veterinary surgery : VS·2025·Frank. Pet Surgeons, United Kingdom·View original on PubMed →
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Original publication title: Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.
- Species:
- dog
Plain-English summary
A group of paraplegic dogs with severe back problems caused by a slipped disc underwent surgery to relieve pressure on their spinal cords. Researchers developed a machine-learning tool that used CT scan images and the dogs' ability to feel pain to predict which dogs would regain the ability to walk after surgery. Out of 214 dogs, about 77% were able to walk again. The machine-learning model was more accurate than just relying on pain perception alone, providing valuable information to help veterinarians make better surgical decisions for these dogs.
People also search for: dog back surgery recovery · paraplegic dog walking again · intervertebral disc extrusion treatment
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) and on the test set of DPN dogs (Test). Deep-pain perception alone served as the control. RESULTS: Recovery of ambulation was recorded in 165/214 dogs (77.1%) after decompressive surgery. The model had an area under the receiver operating characteristic curve (AUC) of .9118 (95% CI: .8366-.9872), accuracy of 86.1% (95% CI: 74.4%-95.4%), sensitivity of 82.4% (95% CI: 68.6%-93.9%), and specificity of 100.0% (95% CI: 100.0%-100.0%) on Test, and an AUC of .7692 (95% CI: .6250-.9000), accuracy of 72.7% (95% CI: 50.0%-90.9%), sensitivity of 53.8% (95% CI: 25.0%-80.0%), and specificity of 100.0% (95% CI: 100.0%-100.0%) on Test. Deep-pain perception had an AUC of .8088 (95% CI: .7273-.8871), accuracy of 69.8% (95% CI: 55.8%-83.7%), sensitivity of 61.8% (95% CI: 45.5%-77.4%), and specificity of 100.0% (95% CI: 100.0%-100.0%), which was different from that of the model (p = .02). CONCLUSION: Noncontrast CT-based radiomics provided prognostic information in dogs with severe spinal cord injury secondary to acute intervertebral disc extrusion. The model outperformed deep-pain perception alone in identifying dogs that recovered ambulation following decompressive surgery. CLINICAL SIGNIFICANCE: Radiomics features from noncontrast CT, when integrated into a multimodal machine-learning algorithm, may be useful as an assistive tool for surgical decision making.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/41035390/