(PR077) Clinical Augmented Reality and Artificial Intelligence to Measure and Localize Cortical Changes Directly from the Human Brain in a Pain Model of Endodontic Origin

This study addresses the challenges of objectively assessing dental pain and anxiety during endodontic procedures. Traditional methods lack accuracy, prompting exploration of neuroimaging techniques focusing on brain activity. The research aimed to quantify dental pain and its evoked stimulation via percussion, correlating patient pain/anxiety with brain functional connectivity. Additionally, the study examined pain modulation by anesthetizing affected teeth and monitoring cortical processing. Using functional near-infrared spectroscopy via a portable optical neuroimaging cap, 20 patients’ cortical activity was measured during dental pain induced by percussion stimulation (8 times consecutively). These subjects (n=20) were all diagnosed with symptomatic apical periodontitis. A neural network-based AI algorithm classified hemodynamic response data into pain and no-pain states. Results showed odontogenic pain activated the primary somatosensory cortex contralaterally, confirming trigeminal homuncular representation. Local anesthetic delivery deactivated the anterior prefrontal cortex bilaterally, significantly decreasing brain functional connectivity due to eliminated sensory input. The neural network-based AI algorithm achieved an overall classification accuracy of 71.9%. In conclusion, this study highlights the activation of the primary somatosensory cortex in response to odontogenic pain, confirming the trigeminal homuncular representation. Additionally, it demonstrates the impact of local anesthetic delivery on resting state functional connectivity in the brain. These findings offer valuable insights into understanding and managing dental pain and anxiety, potentially paving the way for improved clinical approaches and patient experiences during endodontic and other dental procedures.