{"title":"人工智能在牙操作显微镜图像根管口识别中的应用:初步评价。","authors":"E. Karataş, O. Ünal, Ö. Çelik, İ. Ş. Bayrakdar","doi":"10.1111/aej.12955","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25× magnification. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labelled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images.</p>\n </div>","PeriodicalId":55581,"journal":{"name":"Australian Endodontic Journal","volume":"51 2","pages":"407-414"},"PeriodicalIF":1.5000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation\",\"authors\":\"E. Karataş, O. Ünal, Ö. Çelik, İ. Ş. Bayrakdar\",\"doi\":\"10.1111/aej.12955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25× magnification. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labelled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images.</p>\\n </div>\",\"PeriodicalId\":55581,\"journal\":{\"name\":\"Australian Endodontic Journal\",\"volume\":\"51 2\",\"pages\":\"407-414\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Australian Endodontic Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/aej.12955\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australian Endodontic Journal","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/aej.12955","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
摘要
目的:评价人工智能(AI)在牙科操作显微镜(DOM)图像检测根管口中的诊断性能。共有80颗人类上颌第一和第二磨牙被纳入研究。在制备传统通道腔后,在牙科操作显微镜(DOM)下,以21.25倍放大镜对根管孔进行识别。在孔口识别之后,使用DOM获得视频记录,从中随机抽取1527帧进行分析。使用CranioCatch标记软件(CranioCatch, eski ehir,土耳其)手动标记这些框架中的根管孔。在二元分类任务中,系统正确识别了526个根管孔中的502个,准确率为91%。基于yolo的CNN在DOM图像中检测根管孔具有较高的准确性和灵敏度。
Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation
To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25× magnification. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labelled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images.
期刊介绍:
The Australian Endodontic Journal provides a forum for communication in the different fields that encompass endodontics for all specialists and dentists with an interest in the morphology, physiology, and pathology of the human tooth, in particular the dental pulp, root and peri-radicular tissues.
The Journal features regular clinical updates, research reports and case reports from authors worldwide, and also publishes meeting abstracts, society news and historical endodontic glimpses.
The Australian Endodontic Journal is a publication for dentists in general and specialist practice devoted solely to endodontics. It aims to promote communication in the different fields that encompass endodontics for those dentists who have a special interest in endodontics.