人工智能在x光片上识别牙种植体系统。

IF 1.3 4区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE International Journal of Periodontics & Restorative Dentistry Pub Date : 2023-05-01 DOI:10.11607/prd.5781
Chinhua Y Hsiao, Hexin Bai, Haibin Ling, Jie Yang
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引用次数: 0

摘要

医疗保健正在进入一个将数据挖掘应用于人工智能的新时代。在世界范围内,种植牙系统的数量一直在增加。如果没有过去可用的记录,来自不同牙科诊所的患者的移动性会使临床医生对种植体的识别极具挑战性,并且在同一实践中使用可靠的工具来识别各种种植体系统设计将是有利的,因为在牙周病学和修复牙科领域非常需要识别系统。然而,目前还没有使用人工智能/卷积神经网络对植入物属性进行分类的研究。因此,本研究使用人工智能来识别植入物的放射图像属性。在过去9年中,通过各种机器学习网络识别三家种植体制造商及其亚型的平均准确率超过95%。
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Artificial Intelligence in Identifying Dental Implant Systems on Radiographs.

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.

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来源期刊
CiteScore
2.90
自引率
6.20%
发文量
113
审稿时长
6-12 weeks
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.
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