Machine Learning Predicts Patient Tangible Outcomes After Dental Implant Surgery

Abstract

Dental implants have become increasingly important in daily dental offices. The degree of pain and discomfort experienced during a surgical procedure varies from one patient to another. Using advanced machine learning algorithms to predict pain, the dentist and the patient would make more informed decisions about the treatment. This study aims at Predicting postoperative discomfort using an AI-based multi-linear regression model. The functional parametric association between the eight parameters (age, sex, and operating technique) and the patient's postoperative pain was established following implant surgery. The output was normalized information regarding both incidence and severity of immediate discomfort post-implant surgery. To enhance the generalization ability of the multiple linear regression (MLR) model and avoid overfitting, 825 cases were provided as the training set, while 207 cases were given for data authentication. In addition, 45 samples were used as controls to determine the model's prediction accuracy. Evaluation of the given model reveals a Root Mean Squared Error of 0.108477788. This prototype predicted AI model postoperative pain following implant surgery with 89.6 % accuracy. Finally, this AI model exhibited clinical viability and utility in predicting postoperative pain after surgery.

Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain

“…Regression models are a set of methods that utilize ML algorithms to automatically detect patterns and predict outcomes based on input data [35]. In the context of pain research, regression models have the potential to help the diagnosis, classification, and management of pain [36,37]. These models can predict pain occurrence, probability, volatility, and management response via a logistic regression model.…”

“…Several studies have demonstrated the potential of regression models in pain research [37][38][39][40][41], with successful predictions of imaging no-shows, pain severity in patients with primary Sjögren syndrome [39] or juvenile idiopathic arthritis [40], and postoperative discomfort following implant surgery [37].…”

Light gradient boosting-based prediction of quality of life among oral cancer-treated patients

“…Artificial intelligence (A.I.) [6][7][8][9][10][11][12], which imitates human cognitive processes, is a revolutionary technology that has captured the attention of scientists worldwide [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

“…In the contemporary field of dentistry, numerous studies have embraced the integration of digital technologies. Technologies such as cone beam computed tomography (CBCT) imaging [6], surgical guides [7], robotic navigation systems for surgery [8], and even the incorporation of AI in surgery [9] have collectively optimized dental procedures, rendering them more comprehensive and precise. Traditionally, digital panoramic radiography (DPR) has been used to image periodontal conditions and take manual impressions, a process prone to human error.…”