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

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...

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Autores principales:	Veena Basappa Benakatti, Ramesh P Nayakar, Mallikarjun Anandhalli
Formato:	article
Lenguaje:	EN
Publicado:	Wolters Kluwer Medknow Publications 2021
Materias:	artificial intelligence classification dental implants dental radiography machine learning Dentistry RK1-715
Acceso en línea:	https://doaj.org/article/2523f1476dda4a24b74a0212d651b33b
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spelling	oai:doaj.org-article:2523f1476dda4a24b74a0212d651b33b2021-11-19T10:59:31ZMachine learning for identification of dental implant systems based on shape – A descriptive study0972-40521998-405710.4103/jips.jips_324_21https://doaj.org/article/2523f1476dda4a24b74a0212d651b33b2021-01-01T00:00:00Zhttp://www.j-ips.org/article.asp?issn=0972-4052;year=2021;volume=21;issue=4;spage=405;epage=411;aulast=Benakattihttps://doaj.org/toc/0972-4052https://doaj.org/toc/1998-4057Aim: 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.Veena Basappa BenakattiRamesh P NayakarMallikarjun AnandhalliWolters Kluwer Medknow Publicationsarticleartificial intelligenceclassificationdental implantsdental radiographymachine learningDentistryRK1-715ENThe Journal of Indian Prosthodontic Society, Vol 21, Iss 4, Pp 405-411 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence classification dental implants dental radiography machine learning Dentistry RK1-715
spellingShingle	artificial intelligence classification dental implants dental radiography machine learning Dentistry RK1-715 Veena Basappa Benakatti Ramesh P Nayakar Mallikarjun Anandhalli Machine learning for identification of dental implant systems based on shape – A descriptive study
description	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.
format	article
author	Veena Basappa Benakatti Ramesh P Nayakar Mallikarjun Anandhalli
author_facet	Veena Basappa Benakatti Ramesh P Nayakar Mallikarjun Anandhalli
author_sort	Veena Basappa Benakatti
title	Machine learning for identification of dental implant systems based on shape – A descriptive study
title_short	Machine learning for identification of dental implant systems based on shape – A descriptive study
title_full	Machine learning for identification of dental implant systems based on shape – A descriptive study
title_fullStr	Machine learning for identification of dental implant systems based on shape – A descriptive study
title_full_unstemmed	Machine learning for identification of dental implant systems based on shape – A descriptive study
title_sort	machine learning for identification of dental implant systems based on shape – a descriptive study
publisher	Wolters Kluwer Medknow Publications
publishDate	2021
url	https://doaj.org/article/2523f1476dda4a24b74a0212d651b33b
work_keys_str_mv	AT veenabasappabenakatti machinelearningforidentificationofdentalimplantsystemsbasedonshapeadescriptivestudy AT rameshpnayakar machinelearningforidentificationofdentalimplantsystemsbasedonshapeadescriptivestudy AT mallikarjunanandhalli machinelearningforidentificationofdentalimplantsystemsbasedonshapeadescriptivestudy
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Machine learning for identification of dental implant systems based on shape – A descriptive study

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