Automated classification of elongated styloid processes using deep learning models-an artificial intelligence diagnostics.

Abstract

Background: The styloid process (SP), a bony projection from the temporal bone which can become elongated, resulting in cervical pain, throat discomfort, and headaches. Associated with Eagle syndrome, this elongation can compress nearby nerves and blood vessels, leading to potentially severe complications. Traditional imaging-based methods for classifying various types of elongated styloid processes (ESP) are challenging due to variations in image quality, patient positioning, and anatomical differences, which limit diagnostic accuracy. Recent advancements in artificial intelligence, particularly deep learning, provide more efficient classification of elongated styloid processes.

Objective: This study aims to develop an automated classification system for elongated styloid processes using deep learning models and to evaluate the performance of two distinct architectures, EfficientNetB5 and InceptionV3, in classifying elongated styloid processes.

Methods: This retrospective analysis classified elongated styloid processes using Ortho Pantomograms (OPG) sourced from our oral radiology archives. Styloid process lengths were measured using ImageJ software. A dataset of 330 elongated and 120 normal styloid images was curated for deep learning model training and testing. Pre-processing included median filtering and resizing, with data augmentation applied to improve generalization. EfficientNetB5 and InceptionV3 models, utilized as feature extractors, captured unique styloid characteristics. Model performance was evaluated based on accuracy, precision, recall, and F1-score, with a comparative analysis conducted to identify the most effective model and support advancements in patient care.

Results: The EfficientNetB5 model achieved an accuracy of 97.49%, a precision of 98.00%, a recall of 97.00%, and an F1-score of 97.00%, demonstrating strong overall performance. Additionally, the model achieved an AUC of 0.9825. By comparison, the InceptionV3 model achieved an accuracy of 84.11%, a precision of 85.00%, a recall of 84.00%, and an F1-score of 84.00%, with an AUC of 0.8943. This comparison indicates that EfficientNetB5 outperformed InceptionV3 across all key metrics.

Conclusion: In conclusion, our study presents a deep learning-based approach utilizing EfficientNetB5 and InceptionV3 to accurately categorize elongated styloid processes into distinct types based on their morphological characteristics from digital panoramic radiographs. Our results indicate that these models, particularly EfficientNetB5, can enhance diagnostic accuracy and streamline clinical workflows, contributing to improved patient care.