Objective To propose a classification method for ultrasound images of pedicle screw canals based on radiomics analysis, and to evaluate the integrity of the screw canal. Methods With thoracolumbar spine specimens from 4 fresh cadavers, 50 pedicle screw canals were pre-established and ultrasound images of the canals were acquired. A total of 2 000 images (1 000 intact and 1 000 damaged canal samples) were selected. The dataset was randomly divided in a 4∶1 ratio using 5-fold cross-validation to form training and testing sets (consisting of 1 600 and 400 samples, respectively). Firstly, the optimal radius of the region of interest was identified using the Otsu’s thresholding method, followed by feature extraction using pyradiomics. Principal component analysis and the least absolute shrinkage and selection operator algorithm were employed for dimensionality reduction and feature selection, respectively. Subsequently, 3 machine learning models (support vector machine ［SVM］, logistic regression, and random forest) and 3 deep learning models (visual geometry group ［VGG］, ResNet, and Transformer) were used to classify the ultrasound images. The performance of each model was evaluated using accuracy. Results With a region of interest radius of 230 pixels, the SVM model achieved the highest classification accuracy of 96.25%. The accuracy of the VGG model was only 51.29%, while the accuracies of the logistic regression, random forest, ResNet, and Transformer models were 85.50%, 80.75%, 80.17%, and 75.18%, respectively. Conclusion For ultrasound images of pedicle screw canals, the machine learning model performs better than the deep learning model as a whole, and the SVM model has the best classification performance, which can be used to assist physicians in diagnosis.