Journal of dental sciences · 2025
Applications of artificial intelligence in tooth extraction: A systematic review.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Hamada Masakazu, Nomura Ryota, Akitomo Tatsuya, Kusaka Satoru, Iwamoto Yuko, Yamamoto Shiori, Ogaya Yuko, Nakano Kazuhiko
Аннотация
BACKGROUND/PURPOSE: Tooth extraction is a common procedure in dental treatment. In recent years, with the advancement of artificial intelligence (AI) technology, research on tooth extraction using AI has been increasing. In the present study, we consider the applicability of AI to tooth extraction through a literature review.
MATERIALS AND METHODS: The PubMed, Scopus, and Web of Science databases were searched for ("tooth extraction") AND ("artificial intelligence" OR "machine learning" OR "deep learning") in June 2024.
RESULTS: Thirty-five articles matched the eligibility criteria and were extracted for this review. The most widely covered topics were "relationship between the root of the tooth and the inferior alveolar nerve" and "tooth extraction decision-making" with 10 and 8 articles, respectively. These two topics are considered to be important factors that determine risk and treatment options in clinical decision-making. Next, there were six articles about tooth extraction difficulty, preparation, and time, and four articles about maxillary sinus evaluation. Furthermore, there were three articles about predictive models for osteonecrosis and osteomyelitis of the jaw, and two articles each about post-extraction complications and the use of ChatGPT, which were the fewest in number.
CONCLUSION: Findings from these papers will contribute to improving decision-making processes, treatment strategies, and preventive measures in dental care and are expected to serve as a foundation for future research. Furthermore, the diversity of each topic reflects the complexity and evolution of dental care and suggests that further exploration is warranted in future research.
MATERIALS AND METHODS: The PubMed, Scopus, and Web of Science databases were searched for ("tooth extraction") AND ("artificial intelligence" OR "machine learning" OR "deep learning") in June 2024.
RESULTS: Thirty-five articles matched the eligibility criteria and were extracted for this review. The most widely covered topics were "relationship between the root of the tooth and the inferior alveolar nerve" and "tooth extraction decision-making" with 10 and 8 articles, respectively. These two topics are considered to be important factors that determine risk and treatment options in clinical decision-making. Next, there were six articles about tooth extraction difficulty, preparation, and time, and four articles about maxillary sinus evaluation. Furthermore, there were three articles about predictive models for osteonecrosis and osteomyelitis of the jaw, and two articles each about post-extraction complications and the use of ChatGPT, which were the fewest in number.
CONCLUSION: Findings from these papers will contribute to improving decision-making processes, treatment strategies, and preventive measures in dental care and are expected to serve as a foundation for future research. Furthermore, the diversity of each topic reflects the complexity and evolution of dental care and suggests that further exploration is warranted in future research.