Machine Learning Predicts Patient Tangible Outcomes After Dental Implant Surgery

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 d...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.131481-131488
Main Authors: Yadalam, Pradeep Kumar, Trivedi, Smith Sodhan, Krishnamurthi, Indhu, Anegundi, Raghavendra Vamsi, Mathew, Asok, Shayeb, Maher Al, Narayanan, Jayaraj Kodangattil, Jaber, Mohamed A., Rajkumar, R.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Dental implants
Dentistry
Discomfort
Implants
Machine learning
Model accuracy
multi-linear regression model
Pain
Predictive models
Regression analysis
Regression models
Surgery
swelling
technology
Teeth
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Summary: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 0.1085. 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3228793