Awareness of endodontists regarding the determination of root canal morphology and configuration using artificial intelligence

Q1 Medicine
Mohd Irfan Ansari , Neelam Singh , Shahnaz Mansoori , Simran Uppal , Abhishek Mehta , Sweta Rastogi
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引用次数: 0

Abstract

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.
牙髓医生对人工智能确定根管形态和形态的认识
人工智能(AI)在牙髓学领域发展迅速,特别是通过神经网络和深度学习模型的应用,有助于识别复杂的根管形态和配置,从而提高诊断准确性和治疗计划。本研究旨在评估印度牙髓医生对人工智能在确定根管形态和配置方面的应用的认识。方法采用谷歌表格向印度执业牙髓医生发放在线调查问卷,并记录其回答。采用卡方检验研究自变量和因变量的相关性。结果对338名执业牙髓医生和研究生的调查显示,只有不到一半的人知道人工智能模型,如人工神经网络(43.8%)和深度学习(35.2%)。大多数人(68.3%)部分了解人工智能在牙髓学中的应用。约37.9%的人认为人工智能在日常牙髓临床实践中“相当可行”,82.5%的人认为人工智能技术可以提高牙髓治疗成功率(p < 0.001)。然而,60.90%的牙髓医生认为自己没有接受过操作人工智能模型的培训,91.10%的牙髓医生从未使用过任何人工智能模型或软件(p < 0.001)。此外,89.30%的参与者表示需要关于使用人工智能确定根管形态的培训计划和研讨会(p < 0.001)。结论大多数牙髓医生缺乏足够的知识来使用人工智能模型,也没有使用人工智能软件来识别根管形态和配置。本研究强调了对牙髓医生进行适当培训的必要性,以提高人工智能在确定根管形态和形态方面的应用。
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来源期刊
CiteScore
4.90
自引率
0.00%
发文量
133
审稿时长
167 days
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.
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