Artificial Intelligence: A Reliable Tool to Detect the Elongation of the Styloid Process.

ABSTRACT

Background Eagle's syndrome is characterized by the anomalous elongation of the styloid process. This condition is usually identified through the manual evaluation of orthopantomogram (OPG) images, which is time-consuming and can have interobserver variability. The application of Artificial intelligence (AI) in radiology is gaining importance and interest in recent years. The application of AI in detecting styloid process elongation is less explored, advocating for research in the same arena. Aim and objectives The study aimed to evaluate the accuracy of artificial intelligence in detecting styloid process elongation in digital OPGs and to compare the performance of the three different AI algorithms with that of the manual radiographic evaluation by the radiologist. Materials and methods A total of 400 digital OPGs were screened, and linear measurements of the styloid process length (ImageJ software (National Institute of Health, Maryland, USA)) were done for the identification of styloid process elongation by a single calibrated observer to finally include a processed image dataset including 169 images of the elongated styloid process and 200 images of the normal styloid process. A machine learning approach was used to detect the styloid process elongation using the three different AI models logistic regression, neural network, and Naïve Bayes algorithms in Orange software (University of Ljubljana, Slovenia). Performance evaluation was done using the accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC-ROC (area under the receiver operating characteristic) curve. Results Logistic regression and neural network algorithms depicted the highest accuracy of 100% with no false positives or false negatives, securing a score of 1.000 for all the metrics. However, the Naïve Bayes model demonstrated a fairly considerable accuracy, classifying 49 false positive images and 59 false negative images with an AUC (area under the curve) score of 78 %. Nevertheless, it performed better than random guessing. Conclusion Logistic regression and neural network algorithms accurately detected styloid process elongation similar to that of manual radiographic evaluation. The Naïve Bayes algorithm did not perform an accurate classification yet performed better than random guessing. AI holds a promising scope for its application in automatically detecting styloid process elongation in digital OPGs.