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牙科领域的人工智能:过去、现在与未来。

Artificial Intelligence in Dentistry: Past, Present, and Future.

作者信息

Agrawal Paridhi, Nikhade Pradnya

机构信息

Department of Conservative Dentistry and Endodontics, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Medical Sciences University, Wardha, IND.

出版信息

Cureus. 2022 Jul 28;14(7):e27405. doi: 10.7759/cureus.27405. eCollection 2022 Jul.

DOI:10.7759/cureus.27405
PMID:36046326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9418762/
Abstract

Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

摘要

人工智能(AI)在包括牙科在内的广泛领域中的存在感和重要性显著增强。它可以模仿人类智能,在医疗保健领域,尤其是牙髓病学中进行复杂的预测和决策。人工智能模型,如卷积神经网络和/或人工神经网络,已在牙髓病学中展现出多种应用,包括研究根管系统的解剖结构、预测牙髓干细胞的活力、测量工作长度、确定根折和根尖周病变以及预测再治疗程序的成功率。该技术的未来应用涉及日程安排、患者护理、药物相互作用、预后诊断和机器人牙髓手术。在牙髓病学中,就疾病检测、评估和预测而言,人工智能已证明了其准确性和精确性。人工智能有助于牙髓病诊断和治疗的进步,从而提高牙髓病治疗效果。然而,在将人工智能模型纳入常规临床操作之前,进一步验证这些模型的成本效益、可靠性和适用性仍然很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/34b97b618435/cureus-0014-00000027405-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/f229a4824357/cureus-0014-00000027405-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/c638d02e1d22/cureus-0014-00000027405-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/72c2beb71cc4/cureus-0014-00000027405-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/e8c6d2216e8e/cureus-0014-00000027405-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/34b97b618435/cureus-0014-00000027405-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/f229a4824357/cureus-0014-00000027405-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/c638d02e1d22/cureus-0014-00000027405-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/72c2beb71cc4/cureus-0014-00000027405-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/e8c6d2216e8e/cureus-0014-00000027405-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a69/9418762/34b97b618435/cureus-0014-00000027405-i05.jpg

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