{"title":"牙髓医生对人工智能确定根管形态和形态的认识","authors":"Mohd Irfan Ansari , Neelam Singh , Shahnaz Mansoori , Simran Uppal , Abhishek Mehta , Sweta Rastogi","doi":"10.1016/j.jobcr.2025.09.014","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>Artificial intelligence (AI) is rapidly advancing in Endodontics, particularly through the application of neural networks and deep learning models, that help in identifying complex root canal morphology and configurations, thereby enhancing diagnostic accuracy and treatment planning. This study aimed to assess the awareness of Indian Endodontists regarding AI applications in determining root canal morphology and configuration.</div></div><div><h3>Methods</h3><div>An online survey-based questionnaire was distributed to practicing Endodontists across India using Google Forms, and their responses were recorded. Chi-square test was used to study the association of independent and dependent variables.</div></div><div><h3>Results</h3><div>A survey of 338 practicing endodontists and postgraduate students revealed that less than half were aware of AI models such as <em>Artificial Neural Networks</em> (43.8 %) and <em>Deep Learning</em> (35.2 %). The majority (68.3 %) were partially aware of AI applications in endodontics. About 37.9 % considered AI as \"fairly feasible\" for daily endodontic clinical practice, and 82.5 % agreed that AI technology can enhance endodontic treatment success rates (p < 0.001). However, 60.90 % of the endodontists did not consider themselves trained for operating AI models, and 91.10 % never used any AI models or software (p < 0.001). Additionally, 89.30 % of the participants expressed the need for training programs and workshops on the use of AI in determining root canal morphology (p < 0.001).</div></div><div><h3>Conclusion</h3><div>Most Endodontists do not have sufficient knowledge to use AI models and do not employ AI software to identify root canal morphology and configuration. This study highlights the necessity for proper training for endodontists to improve the use of AI in determining root canal morphology and configuration.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1584-1590"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Awareness of endodontists regarding the determination of root canal morphology and configuration using artificial intelligence\",\"authors\":\"Mohd Irfan Ansari , Neelam Singh , Shahnaz Mansoori , Simran Uppal , Abhishek Mehta , Sweta Rastogi\",\"doi\":\"10.1016/j.jobcr.2025.09.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>Artificial intelligence (AI) is rapidly advancing in Endodontics, particularly through the application of neural networks and deep learning models, that help in identifying complex root canal morphology and configurations, thereby enhancing diagnostic accuracy and treatment planning. This study aimed to assess the awareness of Indian Endodontists regarding AI applications in determining root canal morphology and configuration.</div></div><div><h3>Methods</h3><div>An online survey-based questionnaire was distributed to practicing Endodontists across India using Google Forms, and their responses were recorded. Chi-square test was used to study the association of independent and dependent variables.</div></div><div><h3>Results</h3><div>A survey of 338 practicing endodontists and postgraduate students revealed that less than half were aware of AI models such as <em>Artificial Neural Networks</em> (43.8 %) and <em>Deep Learning</em> (35.2 %). The majority (68.3 %) were partially aware of AI applications in endodontics. About 37.9 % considered AI as \\\"fairly feasible\\\" for daily endodontic clinical practice, and 82.5 % agreed that AI technology can enhance endodontic treatment success rates (p < 0.001). However, 60.90 % of the endodontists did not consider themselves trained for operating AI models, and 91.10 % never used any AI models or software (p < 0.001). Additionally, 89.30 % of the participants expressed the need for training programs and workshops on the use of AI in determining root canal morphology (p < 0.001).</div></div><div><h3>Conclusion</h3><div>Most Endodontists do not have sufficient knowledge to use AI models and do not employ AI software to identify root canal morphology and configuration. This study highlights the necessity for proper training for endodontists to improve the use of AI in determining root canal morphology and configuration.</div></div>\",\"PeriodicalId\":16609,\"journal\":{\"name\":\"Journal of oral biology and craniofacial research\",\"volume\":\"15 6\",\"pages\":\"Pages 1584-1590\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of oral biology and craniofacial research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212426825002283\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of oral biology and craniofacial research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212426825002283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Awareness of endodontists regarding the determination of root canal morphology and configuration using artificial intelligence
Objective
Artificial intelligence (AI) is rapidly advancing in Endodontics, particularly through the application of neural networks and deep learning models, that help in identifying complex root canal morphology and configurations, thereby enhancing diagnostic accuracy and treatment planning. This study aimed to assess the awareness of Indian Endodontists regarding AI applications in determining root canal morphology and configuration.
Methods
An online survey-based questionnaire was distributed to practicing Endodontists across India using Google Forms, and their responses were recorded. Chi-square test was used to study the association of independent and dependent variables.
Results
A survey of 338 practicing endodontists and postgraduate students revealed that less than half were aware of AI models such as Artificial Neural Networks (43.8 %) and Deep Learning (35.2 %). The majority (68.3 %) were partially aware of AI applications in endodontics. About 37.9 % considered AI as "fairly feasible" for daily endodontic clinical practice, and 82.5 % agreed that AI technology can enhance endodontic treatment success rates (p < 0.001). However, 60.90 % of the endodontists did not consider themselves trained for operating AI models, and 91.10 % never used any AI models or software (p < 0.001). Additionally, 89.30 % of the participants expressed the need for training programs and workshops on the use of AI in determining root canal morphology (p < 0.001).
Conclusion
Most Endodontists do not have sufficient knowledge to use AI models and do not employ AI software to identify root canal morphology and configuration. This study highlights the necessity for proper training for endodontists to improve the use of AI in determining root canal morphology and configuration.
期刊介绍:
Journal of Oral Biology and Craniofacial Research (JOBCR)is the official journal of the Craniofacial Research Foundation (CRF). The journal aims to provide a common platform for both clinical and translational research and to promote interdisciplinary sciences in craniofacial region. JOBCR publishes content that includes diseases, injuries and defects in the head, neck, face, jaws and the hard and soft tissues of the mouth and jaws and face region; diagnosis and medical management of diseases specific to the orofacial tissues and of oral manifestations of systemic diseases; studies on identifying populations at risk of oral disease or in need of specific care, and comparing regional, environmental, social, and access similarities and differences in dental care between populations; diseases of the mouth and related structures like salivary glands, temporomandibular joints, facial muscles and perioral skin; biomedical engineering, tissue engineering and stem cells. The journal publishes reviews, commentaries, peer-reviewed original research articles, short communication, and case reports.