EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE-BASED SOFTWARE FOR OPTIMIZING DIGITAL REGISTRATION OF STATIC OCCLUSAL RELATIONSHIPS DURING INTRAORAL SCANNING
DOI:
https://doi.org/10.11603/2311-9624.2026.3.16572Keywords:
intraoral scanning, occlusion, occlusal contacts, dental rehabilitation, artificial intelligence, prosthetic treatmentAbstract
Accurate registration of static occlusal relationships between the maxilla and mandible remains relevant challenge in modern digital dentistry. This is due to the need to account not only for existing alterations in the occlusal scheme that require compensation, but also for the physiological parameters of tooth micromobility resulting from the resilience of the periodontal tissue complex. Magnitude of such mobility at the individual level cannot be precisely predicted within the processing algorithms of software associated with intraoral scanning systems. Aim of the study. To evaluate the practical effectiveness of artificial intelligence–based software in minimizing effect of occlusal collisions while articulating intraoral scans within a digital dental rehabilitation protocol. Research materials and methods. The study compared the number of occlusal collisions (graphical interpenetrations) identified in 64 pairs of aligned intraoral scans of maxilla and mandible. In the control group static occlusal relationships were registered within the original software of the scanning system without the use of additional correction functions. In the study group occlusal relationships were optimized using software operating on artificial intelligence algorithms. All scans were obtained using the Medit i500 intraoral scanning system. Results. Processing of intraoral scan pairs and initial digital bite registrations using artificial intelligence–based software resulted in lower total number of occlusal collisions compared to processing scans within the original intraoral scanning system software; difference in collisions amount between two approaches was statistically significant (p < 0,05). After processing the scans within the BiteFinder software differences in the number of occlusal collisions in cases with and without dental arch defects of varying extent and localization, and/or with varying numbers and locations of prepared teeth, were not statistically significant (p > 0,05). Conclusions. Application of artificial intelligence-based software for the optimization of static occlusal relationships based on intraoral scanning data contributed to statistically significant reduction in the number of occlusal interferences (graphical interpenetrations) compared to results obtained solely within the original intraoral scanning system software environment. At the same time, the number of occlusal interferences cannot be regarded as a direct equivalent of trueness and precision indicators in reproducing interocclusal relationships. Therefore, occlusal interferences should be considered an additional, clinically oriented criterion for evaluating the results of digital bite registration, which is complementing but not replacing trueness and precision metrics.
References
1. Siqueira R., Galli M., Chen Z., Mendonça G. et al. Intraoral scanning reduces procedure time and improves patient comfort in fixed prosthodontics and implant dentistry: a systematic review. Clinical oral investigations. 2021. Vol. 25(12). P. 6517–6531. https://doi.org/10.1007/s00784-021-04157-3. DOI: https://doi.org/10.1007/s00784-021-04157-3
2. Vieira F. L., Carnietto M., Cerqueira Filho J. R. et al. Intraoral Scanning Versus Conventional Methods for Obtaining Full-Arch Implant-Supported Prostheses: A Systematic Review with Meta-Analysis. Applied Sciences. 2025. Vol. 15(2). P. 533. https://doi.org/10.3390/app15020533. DOI: https://doi.org/10.3390/app15020533
3. Mangano F., Gandolfi A., Luongo G., Logozzo S. Intraoral scanners in dentistry: a review of the current literature. BMC oral health. 2017. Vol. 17(1). P. 149. https://doi.org/10.1186/s12903-017-0442-x. DOI: https://doi.org/10.1186/s12903-017-0442-x
4. Albanchez-Gonzalez M. I., Brinkmann J. C. B., Pelaez-Rico J. et al. Accuracy of digital dental implants impression taking with intraoral scanners compared with conventional impression techniques: A systematic review of in vitro studies. International journal of environmental research and public health. 2022. Vol. 19(4). P. 2026. https://doi.org/10.3390/ijerph19042026. DOI: https://doi.org/10.3390/ijerph19042026
5. Pesce P., Nicolini P., Caponio V. C. A. et al. Accuracy of full-arch intraoral scans versus conventional impression: a systematic review with a meta-analysis and a proposal to standardise the analysis of the accuracy. Journal of Clinical Medicine. 2024. Vol. 14(1). P. 71. https://doi.org/10.3390/jcm14010071. DOI: https://doi.org/10.3390/jcm14010071
6. Ma J., Zhang B., Song H. et al. Accuracy of digital implant impressions obtained using intraoral scanners: A systematic review and meta-analysis of in vivo studies. International Journal of Implant Dentistry. 2023. Vol. 9(1). P. 48. https://doi.org/10.1186/s40729-023-00517-8. DOI: https://doi.org/10.1186/s40729-023-00517-8
7. Joensahakij N., Serichetaphongse P., Chengprapakorn W. The accuracy of conventional versus digital (intraoral scanner or photogrammetry) impression techniques in full-arch implant-supported prostheses: a systematic review. Evidence-Based Dentistry. 2024. Vol. 25(4). P. 216–217. https://doi.org/10.1038/s41432-024-01045-z. DOI: https://doi.org/10.1038/s41432-024-01045-z
8. Chen H. T., Feng S. W., Vo T. T. T. et al. Cumulative Error in Digital Workflows for Full-Arch Implant Rehabilitation: A Narrative Review. Bioengineering. 2026. Vol. 13(2). P. 219. https://doi.org/10.3390/bioengineering13020219. DOI: https://doi.org/10.3390/bioengineering13020219
9. Revilla‐León M., Aragoneses R., Arroyo Valverde E. M. et al. Classification of scanning errors of digital scans recorded by using intraoral scanners. Journal of Esthetic and Restorative Dentistry. 2025. Vol. 37(6). P. 1363–1371. https://doi.org/10.1111/jerd.13419. DOI: https://doi.org/10.1111/jerd.13419
10. Putra R. H., Doi C., Yoda N. et al. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiology. 2022. Vol. 51(1). P. 20210197. https://doi.org/10.1259/dmfr.20210197. DOI: https://doi.org/10.1259/dmfr.20210197
11. Najeeb M., Islam S. Artificial intelligence (AI) in restorative dentistry: current trends and future prospects. BMC Oral Health. 20225. Vol. 25(1). P. 592. https://doi.org/10.1186/s12903-025-05989-1. DOI: https://doi.org/10.1186/s12903-025-05989-1
12. Azhari A. A., Helal N., Sabri L. M., Abduljawad A. Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition. Digital Health. 2023. Vol. 9. P. 20552076231216681. https://doi.org/10.1177/20552076231216681. DOI: https://doi.org/10.1177/20552076231216681
13. Chinam N., Bekkali M., Kallas M., Li J. Virtual occlusal records acquired by using intraoral scanners: A review of factors that influence maxillo‐mandibular relationship accuracy. Journal of Prosthodontics. 2023. Vol. 32(S2). P. 192–207. https://doi.org/10.1111/jopr.13787. DOI: https://doi.org/10.1111/jopr.13787
14. Revilla-León M., Barmak A. B., Tohme H. et al. Factors that influence the accuracy of maxillomandibular relationship at maximum intercuspation acquired by using intraoral scanners: A systematic review. Journal of dentistry. 2023. Vol. 138. P. 104718. https://doi.org/10.1016/j.jdent.2023.104718. DOI: https://doi.org/10.1016/j.jdent.2023.104718
15. Nusser A. Intraorale Scan-Daten-Korrektur fehlerhafter Bisslagen: Arbeiten mit der Software Bite-Finder. Quintessenz der Zahntechnik. 2024. Vol. 50(6). P. 636–642.
16. Kordaß B., Ruge S., Imhoff B. et al. Digital Occlusion and Occlusal Analysis-Definitions and Requirements Position Paper of the Study Group for Oral Physiology and Masticatory Function (DGFDT). Journal of Craniomandibular Function. 2024. Vol. 16(2). P. 117–132.
17. Revilla-León M., Gómez-Polo M., Barmak A. B. et al. Accuracy of an artificial intelligence-based program for locating the maxillomandibular relationship of scans acquired by using intraoral scanners. The Journal of Prosthetic Dentistry. 2024. Vol. 134(3). P. 809–817. https://doi.org/10.1016/j.prosdent.2024.01.023. DOI: https://doi.org/10.1016/j.prosdent.2024.01.023
18. Revilla-León M., Fernández-Estevan L., Barmak A. B. et al. Accuracy of maximum intercuspal position located by using four intraoral scanners and an artificial intelligence-based program. The Journal of Prosthetic Dentistry. 2024. Vol. 134(4). P. 1242–1250. https://doi.org/10.1016/j.prosdent.2024.03.007. DOI: https://doi.org/10.1016/j.prosdent.2024.03.007
19. Ntovas P., Vazouras K., Barmak A. B. et al. Influence of artificial intelligence-based registration and occlusal collision correction on the accuracy of occlusal contacts captured at maximum intercuspal position using an intraoral scanner: A clinical comparative study. The Journal of Prosthetic Dentistry. 2025. P. S0022-3913(25)00832-7. https://doi.org/10.1016/j.prosdent.2025.10.028. DOI: https://doi.org/10.1016/j.prosdent.2025.10.028
20. Revilla-León M., Gómez-Polo M., Barmak A. B. et al. Influence of scan extension on the accuracy of maximum intercuspal position recorded by using intraoral scanners or an artificial intelligence-based program. The Journal of Prosthetic Dentistry. 2025. Vol. 134(6). P. 2524–2533. https://doi.org/10.1016/j.prosdent.2025.01.012. DOI: https://doi.org/10.1016/j.prosdent.2025.01.012
21. Гончарук-Хомин М. Ю., Тукало І. В., Лях А. І., Стойка В. В. Фактори впливу та підходи до оптимізації оцінки міжоклюзійних співвідношень верхньої та нижньої щелеп із використанням даних інтраорального сканування. Вісник стоматології. 2024. № 127(2). С. 107–120. https://doi.org/10.35220/2078-8916-2024-52-2.17. DOI: https://doi.org/10.35220/2078-8916-2024-52-2.17
