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Year : 2021  |  Volume : 21  |  Issue : 4  |  Page : 405-411

Machine learning for identification of dental implant systems based on shape – A descriptive study

1 Department of Prosthodontics and Crown and Bridge, KAHER'S KLE VK Institute of Dental Sciences, Belagavi, Karnataka, India
2 Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India

Correspondence Address:
Veena Basappa Benakatti
Department of Prosthodontics and Crown and Bridge, KAHER'S KLE VK Institute of Dental Sciences, Neharu Nagar, Belagavi - 590 010, Karnataka
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jips.jips_324_21

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Aim: To evaluate the efficacy of machine learning in identification of dental implant systems from panoramic radiographs based on the shape. Settings and Design: In vitro–Descriptive study Materials and Methods: A Dataset of digital panoramic radiographs of three dental implant systems were obtained. The images were divided into two datasets: one for training and another for testing of the machine learning models. Machine learning algorithms namely, support vector machine, logistic regression, K Nearest neighbor and X boost classifiers were trained to classify implant systems from radiographs, based on the shape using Hu and Eigen values. Performance of algorithms was evaluated by its classification accuracy using the test dataset. Statistical Analysis Used: Accuracy and recover operating characteristic (ROC) curve were calculated to analyze the performance of the model. Results: The classifiers tested in the study were able to identify the implant systems with an average accuracy of 0.67. Of the classifiers trained, logistic regression showed best overall performance followed by SVM, KNN and X boost classifiers. Conclusions: Machine learning models tested in the study are proficient enough to identify dental implant systems; hence we are proposing machine learning as a method for implant identification and can be generalized with a larger dataset and more cross sectional studies.

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