An artificial neural network approach for rational decision-making in borderline orthodontic cases: A preliminary analytical observational in silico study

Abstract

Introduction: Artificial intelligence (AI) technology has transformed the way healthcare functions in the present scenario. In orthodontics, expert systems and machine learning have aided clinicians in making complex, multifactorial decisions. One such scenario is an extraction decision in a borderline case. Objective: The present in silico study was planned with the intention of building an AI model for extraction decisions in borderline orthodontic cases. Design: An observational analytical study. Setting: Department of Orthodontics, Hitkarini Dental College and Hospital, Madhya Pradesh Medical University, Jabalpur, India. Methods: An artificial neural network (ANN) model for extraction or non-extraction decisions in borderline orthodontic cases was constructed based on a supervised learning algorithm using the Python (version 3.9) Sci-Kit Learn library and feed-forward backpropagation method. Based on 40 borderline orthodontic cases, 20 experienced clinicians were asked to recommend extraction or non-extraction treatment. The decision of the orthodontist and the diagnostic records, including the selected extraoral and intra-oral features, model analysis and cephalometric analysis parameters, constituted the training dataset of AI. The built-in model was then tested using a testing dataset of 20 borderline cases. After running the model on the testing dataset, the accuracy, F1 score, precision and recall were calculated. Results: The present AI model showed an accuracy of 97.97% for extraction and non-extraction decision-making. The receiver operating curve (ROC) and cumulative accuracy profile showed a near-perfect model with precision, recall and F1 values of 0.80, 0.84 and 0.82 for non-extraction decisions and 0.90, 0.87 and 0.88 for extraction decisions. Limitation: As the present study was preliminary in nature, the dataset included was too small and population-specific. Conclusion: The present AI model gave accurate results in decision-making capabilities related to extraction and non-extraction treatment modalities in borderline orthodontic cases of the present population.

Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning

“…Within AI, ML utilizes a set of inputs and outputs to create an algorithm to process the data and correctly predict the output [ 19 ]. AI and ML have been utilized for several tasks in orthodontics, such as for automated cephalometric analyses [ 20 , 21 , 22 , 23 , 24 ], predicting extraction vs. non-extraction treatment decisions [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], predicting orthodontic extraction patterns [ 34 ], determining the need for surgery in Class III patients [ 35 ], and growth assessment [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. However, little research has been conducted on the use of AI to predict mandibular growth.…”