Toneva Stojmenova, Verica and Kokalanov, Vasko and Popovska, Lidija and Kovacevska, Ivona and Toneva Nikolova, Aleksandra and Arsovski, Ljupka and Nikolovski, Bruno and Atanasova, Sandra and Panajotu, Risto and Naskova, Sanja (2025) Artificial intelligence in intact tooth model creating and strength testing. In: FDI World Dental Congress 2025 (FDIWDC25), 09-12 Sept 2025, Shanghai, China.
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Abstract
Aim/Purpose
Artificial intelligence (AI) is becoming an increasingly common, widely used tool for intact tooth 3D modeling from CBCT scans. Also, AI could be used for biomechanics testing, with high value of accurate.
Materials and methods
CBCT of the patient was made. After exporting the DICOM format of CBCT in 3D Slicer the upper left central incisor was isolated as volumetric finite element model from the maxilla and then its mesh was corrected and refined in 3D Slicer and Meshmixer. The next step was to convert the exported ASCII code from Meshmixer into appropriate ASCII code for SOFiSTiK with the help of artificial intelligence. The 3D intact tooth model was made with all biological tissue properties. With that, the volumetric model was ready and subjected to strength testing with SOFiSTiK.
Results
The fracture resistance testing was conducted in SOFiSTiK. The results have shown that the intact tooth has a high value of strength, around 1350 N. This value is near to value of in vitro testing with Universal Testing Machine.
Conclusion
Today, in vitro fracture resistance studies have been totally replaced with artificial intelligence algorithms studies. This way of testing the fracture strength could save money and time with value of accurate as in vitro testing.
| Item Type: | Conference or Workshop Item (Poster) |
|---|---|
| Impact Factor Value: | 3.7 |
| Subjects: | Engineering and Technology > Mechanical engineering Engineering and Technology > Medical engineering Medical and Health Sciences > Other medical sciences |
| Divisions: | Faculty of Medical Science |
| Depositing User: | Verica Toneva Stojmenova |
| Date Deposited: | 30 Jan 2026 10:37 |
| Last Modified: | 30 Jan 2026 10:37 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/37105 |
