Peer-Reviewed Publication
J Dent2025;156105695.May 1, 2025Journal Article

A comparative study of digital and conventional occlusal indicators: accuracy and reliability of the T-Scan Novus, wax occlusogram, and articulating silk in clinical application.

Karoline M Reich1, Valerie Tatzber2, Astrid Skolka2, Eva Piehslinger2, Stefan Lettner3, Michael Kundi4, Benedikt Sagl5
1Division of Prosthodontics, University Clinic of Dentistry, Medical University of Vienna, 1090, Vienna, Austria; Karl Donath Laboratory, University Clinic of Dentistry, Medical University of Vienna, 1090, Vienna, Austria; Austrian Cluster for Tissue Regeneration, 1200, Vienna, Austria. Electronic address: karoline.reich@meduniwien.ac.at.
2Division of Prosthodontics, University Clinic of Dentistry, Medical University of Vienna, 1090, Vienna, Austria.
3Karl Donath Laboratory, University Clinic of Dentistry, Medical University of Vienna, 1090, Vienna, Austria; Austrian Cluster for Tissue Regeneration, 1200, Vienna, Austria.
4Department of Environmental Health, Medical University of Vienna, 1090, Vienna, Austria.
5Austrian Cluster for Tissue Regeneration, 1200, Vienna, Austria; Competence Center Artificial Intelligence in Dentistry, University Clinic of Dentistry, Medical University of Vienna, 1090, Vienna, Austria. Electronic address: benedikt.sagl@meduniwien.ac.at.

Abstract

OBJECTIVES: Conventional occlusal analysis methods are increasingly challenged by digital technologies. The purpose of this study was to compare the accuracy and reliability of currently available conventional and digital occlusal indicators. METHODS: The number and distribution of occlusal contacts in habitual intercuspation (ICP) were recorded in 26 dentate adults using articulating silk (refer…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.