Author(s)

Liu Dapeng ¹

Affiliation(s)

¹ SMU, Singapore Management University, 574045, Singapore

Corresponding Author

Liu Dapeng

ABSTRACT

Intraoral scanning technology has become an essential tool in current digital oral medicine, with rapid technological development and increasingly widespread clinical applications. This article reviews the development process of intraoral scanning technology, classifies and introduces the principles of commonly used intraoral scanning technology, and briefly explains the application of this technology in digital diagnosis and treatment in different fields of dentistry. The author analyzes and compares the scanning accuracy of five different types of oral scanners for scanning single jaw complete dentition plaster models. And evaluate the scanning quality to provide reference for clinical application and provide a basis for further improving the performance of domestic oral scanners in the future. The author used a high-precision desktop scanner (Yunjia UP560) to obtain a digital model and used it as truth group data. After using the analysis software Geomagic Studio14 for "best fit comparison", the author conducted deviation analysis on the true value group and experimental group data, evaluated the quality indicators of the scanned data, and compared the scanning accuracy. In terms of scanning accuracy, international manufacturers represented by iTeroElement1 and 3ShapeTrios3 are both at a high level. The Fusion Scanner, Aoralscan2, and Mediti500 instruments have different advantages in accuracy and precision across different measurement ranges. The accuracy of scanning single tooth crowns with several instruments is better than that of scanning single jaw full dentition, indicating that reducing the scanning range can improve the accuracy of the scanner.

KEYWORDS

Intraoral scanner; Digital technology; Digital dentistry; Precision; Scanning quality evaluation

CITE THIS PAPER

Liu Dapeng, Application of Artificial Intelligence Graphics and Intraoral Scanning in Medical Scenes. Journal of Artificial Intelligence Practice (2023) Vol. 6: 1-12. DOI: http://dx.doi.org/10.23977/jaip.2023.060601.

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