Predictive modelling of oral flail using machine learning with digital technology.

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K18645
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 57050:Prosthodontics-related
• Research Institution: Showa University
• Principal Investigator: Sanda Minoru 昭和大学, 歯学部, 助教 (10817612)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: デジタル歯科 / 光学印象 / オーラルフレイル

Outline of Final Research Achievements

In recent years, along with the remarkable evolution of artificial intelligence, the application of machine learning, which enables computers to perform functions similar to the learning ability of humans, to medical treatment has been attempted. In this study, as an initial step towards establishing a diagnosis system for prosthetic dentistry based on machine learning, we analysed objective quantitative data on tooth loss by machine learning using patients' digital dental data and clinical information obtained from an intraoral scanner, and conducted research with a view to constructing a prediction model for oral frailty. The study was conducted with a view to building a prediction model for oral frailty. Research focused on impression accuracy, which is important for the construction of an information infrastructure using data acquired by intraoral scanners, and was presented at conferences and papers were written.

Free Research Field

歯科補綴学

Academic Significance and Societal Importance of the Research Achievements

STLデータによる欠損歯列の三次元的予測モデルを構築すれば，従来の解析手法とは異なる，新たな相互関連性が見出だされ予知性の高い治療方針の設定が可能になると考えられる. 補綴歯科領域におけるデジタルデータの活用は未だ発展途上であり，本研究には学術的意義がある.
本研究により客観的なデータベースに基づく診断が進めば、従来経験的に行われてきた欠損患者に対する治療オプションのディシジョンメイキングについて新たな提案をすることが可能であり，社会的意義がある.