Evaluation of ultrasonic images of pedicle screw channel based on machine learning

Objective To explore an ultrasonic image identification and verification method for the integrity of pedicle screw channel based on support vector machine (SVM). Methods Four fresh human cadavers were used to pre-establish 50 screw channels and obtain ultrasonic images of pedicle screw channels. A total of 800 images (400 damaged and 400 intact samples) were selected. The data of the samples were expanded by the method of 5-fold cross-validation to obtain a sample set, and an artificial intelligence-aided diagnosis model for intelligent analysis of ultrasonic images was established. The specific method was as follows: firstly, the ultrasonic images which were easy to be judged and recognized by computer were obtained by image enhancement method, then the texture features of the images were taken as the first type of features, and the SVM model was used to build the initial classification model of intact and damaged samples. Secondly, the threshold T which was used to distinguish the foreground and background was obtained by gray distribution, and the radius R of the concentric circle of the nail track was obtained by the designed loss function. Finally, the entropy, variance, contrast, energy and average absolute deviation of the external image of the concentric circle were taken as the second classification features, and the secondary classification models of the slightly damaged samples and the intact samples were built. The classification results were evaluated by the accuracy, specificity, sensitivity, F1 value, false positive rate, and false negative rate. Results The accuracy of the initial classification was 74.75%, the specificity was 68.00%, the sensitivity was 81.50%, the F1 value was 76.35%, the false positive rate was 32.00%, and the false negative rate was 18.50%. The threshold T calculated before the second classification was 37, and the optimal radius R was 108 pixels. The accuracy of secondary classification was 94.25%, the specificity was 91.00%, the sensitivity was 97.50%, the F1 value was 94.43%, the false positive rate was 9.00%, and the false negative rate was 2.50%. The accuracy of the secondary classification was 19.50% higher than that of the initial classification. Conclusion The artificial intelligence-aided diagnosis model based on SVM can improve the judgment ability of the ultrasonic image of pedicle screw canal damage.