Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images.
Authors
Affiliations (7)
Affiliations (7)
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
- Department of Biostatistics, Faculty of Medicine & Operational Research Centre in Healthcare, Near East University, Nicosia/TRNC, Turkey.
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Turkey.
- Department of Clinical Sciences, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
- Nitte (Deemed to be University), Department of Oral Medicine and Radiology, AB Shetty Memorial Institute of Dental Sciences (ABSMIDS), Mangalore, Karnataka, India.
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates.
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates. Electronic address: [email protected].
Abstract
The objective of the present study was to determine the accuracy of machine learning (ML) models in the detection of mesiobuccal (MB2) canals in axial cone-beam computed tomography (CBCT) sections. A total of 2500 CBCT scans from the oral radiology department of University Dental Hospital, Sharjah were screened to obtain 277 high-resolution, small field-of-view CBCT scans with maxillary molars. Among the 277 scans, 160 of them showed the presence of MB2 orifice and the rest (117) did not. Two-dimensional axial images of these scans were then cropped. The images were classified and labelled as N (absence of MB2) and M (presence of MB2) by 2 examiners. The images were embedded using Google's Inception V3 and transferred to the ML classification model. Six different ML models (logistic regression [LR], naïve Bayes [NB], support vector machine [SVM], K-nearest neighbours [Knn], random forest [RF], neural network [NN]) were then tested on their ability to classify the images into M and N. The classification metrics (area under curve [AUC], accuracy, F1-score, precision) of the models were assessed in 3 steps. NN (0.896), LR (0.893), and SVM (0.886) showed the highest values of AUC with specified target variables (steps 2 and 3). The highest accuracy was exhibited by LR (0.849) and NN (0.848) with specified target variables. The highest precision (86.8%) and recall (92.5%) was observed with the SVM model. The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. It was also observed that when the target variable was specified, significant success rates such as 86.8% in precision and 92.5% in recall were achieved. The present study showed promising results in the ML-based detection of MB2 canal using axial CBCT slices.