{"title":"Artificial Intelligence in Identifying Dental Implant Systems on Radiographs.","authors":"Chinhua Y Hsiao, Hexin Bai, Haibin Ling, Jie Yang","doi":"10.11607/prd.5781","DOIUrl":null,"url":null,"abstract":"<p><p>Health care is entering a new era where data mining is applied to artificial intelligence. The number of dental implant systems has been increasing worldwide. Patient mobility from different dental offices can make identification of implants for clinicians extremely challenging if there are no past available records, and it would be advantageous to use a reliable tool to identify the various implant system designs in the same practice, as there is a great need for identifying the systems in the field of periodontology and restorative dentistry. However, there have not been any studies devoted to using artificial intelligence/convolutional neural networks to classify implant attributes. Thus, the present study used artificial intelligence to identify the attributes of radiographic images of implants. An average accuracy rate of over 95% was achieved with various machine learning networks to identify three implant manufacturers and their subtypes placed during the past 9 years.</p>","PeriodicalId":54948,"journal":{"name":"International Journal of Periodontics & Restorative Dentistry","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Periodontics & Restorative Dentistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.11607/prd.5781","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
Health care is entering a new era where data mining is applied to artificial intelligence. The number of dental implant systems has been increasing worldwide. Patient mobility from different dental offices can make identification of implants for clinicians extremely challenging if there are no past available records, and it would be advantageous to use a reliable tool to identify the various implant system designs in the same practice, as there is a great need for identifying the systems in the field of periodontology and restorative dentistry. However, there have not been any studies devoted to using artificial intelligence/convolutional neural networks to classify implant attributes. Thus, the present study used artificial intelligence to identify the attributes of radiographic images of implants. An average accuracy rate of over 95% was achieved with various machine learning networks to identify three implant manufacturers and their subtypes placed during the past 9 years.
期刊介绍:
The International Journal of Periodontics & Restorative Dentistry will
publish manuscripts concerned with all aspects of clinical periodontology,
restorative dentistry, and implantology. This includes pertinent research
as well as clinical methodology (their interdependence and relationship
should be addressed where applicable); proceedings of relevant symposia
or conferences; and quality review papers. Original manuscripts are considered for publication on the condition that they have not been published
or submitted for publication elsewhere.