Deep Learning-Based Prediction Model of Surgical Indication of Nasal Bone Fracture Using Waters' View

Abstract

Background/Objectives: The nasal bone is critical to both the functional integrity and esthetic contour of the facial skeleton. Nasal bone fractures constitute the most prevalent facial fracture presentation in emergency departments. The identification of these fractures and the determination of immediate intervention requirements pose significant challenges for inexperienced residents, potentially leading to oversight. Methods: A retrospective analysis was conducted on facial trauma patients undergoing cranial radiography (Waters' view) during initial emergency department assessment between March 2008 and July 2022. This study incorporated 2099 radiographic images. Surgical indications comprised the displacement angle, interosseous gap size, soft tissue swelling thickness, and subcutaneous emphysema. A deep learning-based artificial intelligence (AI) algorithm was designed, trained, and validated for fracture detection on radiographic images. Model performance was quantified through accuracy, precision, recall, and F1 score. Hyperparameters included the batch size (20), epochs (70), 50-layer network architecture, Adam optimizer, and initial learning rate (0.001). Results: The deep learning AI model employing segmentation labeling demonstrated 97.68% accuracy, 82.2% precision, 88.9% recall, and an 85.4% F1 score in nasal bone fracture identification. These outcomes informed the development of a predictive algorithm for guiding conservative versus surgical management decisions. Conclusions: The proposed AI-driven algorithm and criteria exhibit high diagnostic accuracy and operational efficiency in both detecting nasal bone fractures and predicting surgical indications, establishing its utility as a clinical decision-support tool in emergency settings.