ARTIFICIAL INTELLIGENCE-BASED DIAGNOSTIC METHODS IN PATIENTS WITH COMBINED INJURIES OF THE MAXILLOFACIAL SKELETON
Keywords:
Keywords: artificial intelligence, maxillofacial trauma, diagnosis, computed tomography, machine learning, facial fractures.Abstract
Abstract. Combined injuries of the maxillofacial skeleton represent complex traumatic conditions requiring accurate and rapid diagnosis. Traditional diagnostic methods, although effective, may be limited in cases of multi-structural damage. In recent years, artificial intelligence (AI) has emerged as a promising tool in medical imaging and trauma diagnosis. This study evaluates the role of AI-based diagnostic methods in patients with combined maxillofacial injuries. A total of 140 patients were analyzed using clinical examination, computed tomography (CT), and AI-assisted image interpretation systems. The results demonstrated that AI significantly improves diagnostic accuracy, reduces interpretation time, and enhances the detection of complex fracture patterns. The study confirms that AI-based systems can serve as an effective auxiliary tool in maxillofacial trauma diagnostics, improving clinical decision-making and treatment planning.
