MODERN METHODS FOR PREDICTING COMPLICATIONS OF PURULENTINFLAMMATORY ODONTOGENIC PROCESSES IN THE MAXILLOFACIAL AREA

Abstract

Purulent-inflammatory odontogenic processes in the maxillofacial region can present significant risks, particularly when complications lead to life-threatening conditions such as sepsis, airway obstruction, or systemic infection. The timely and accurate prediction of these complications is critical for appropriate treatment planning and patient management. Recent advances in diagnostic technologies have greatly enhanced the ability to predict and mitigate these risks. Imaging techniques, such as high-resolution CT scans, MRIs, and 3D imaging, offer detailed visualization of the infected areas, allowing for precise assessment of the infection's extent and potential spread to surrounding tissues. Molecular biomarkers are being increasingly used to identify specific pathogens and gauge the severity of the infection, providing valuable information for selecting targeted therapies. Artificial intelligence (AI) systems, trained on large datasets, can analyze clinical images and patient data to detect patterns and predict the likelihood of complications, offering support in clinical decisionmaking. Additionally, clinical scoring models, which evaluate patient history, physical examination findings, and laboratory results, help clinicians assess the risk of adverse outcomes. Together, these innovations allow for faster, more accurate diagnoses and more effective treatment strategies, ultimately improving patient outcomes, reducing morbidity, and preventing life-threatening complications in individuals with odontogenic infections