Zur Hauptnavigation wechseln Zur Suche wechseln Zum Hauptinhalt wechseln

Prediction of Subsurface Fatigue Damage in Dental CAD/CAM Restorations: Intraoral Scanning vs. Optical Coherence Tomography

Christoph Moos, Julie-Jacqueline Kuhl, Bernd Wöstmann, Christin Grill, Ralf Brinkmann, Maximiliane Amelie Schlenz

Abstract

This study extended a previously established intraoral scanning (IOS) and optical coherence tomography (OCT) dual-modality monitoring workflow for computer-aided design/computer-aided manufacturing (CAD/CAM) restorations to three additional crown material classes alongside a resin composite (RECO) reference. Four material classes were investigated (n=8 each): RECO, polymer-infiltrated ceramic network (PICN), lithium disilicate ceramic (LDSC), and zirconia-reinforced lithium silicate ceramic (ZLSC). Monolithic crowns were adhesively luted to standardized human molar abutment teeth and aged by cyclic loading (50500N, 2Hz, 37 2∘C, up to 1250000 cycles) in a mouth-motion simulator. IOS and handheld OCT were performed at baseline and after every 250000 cycles under phantom-head conditions; correspondence was assessed using Spearman’s rank correlation coefficient (exploratory, uncorrected for multiple comparisons). OCT consistently showed higher defect extents than IOS across all material classes and timepoints. While no significant IOS-OCT associations were found for RECO and the PICN, OCT detected full-thickness vertical subsurface damage propagation from the earliest timepoint in LDSC and ZLSC, with IOS-derived surface wear remaining markedly lower. Surface-based monitoring alone did not reliably reflect subsurface damage propagation, a dissociation most pronounced in the vertical dimension and silicate-based materials. Intraoral OCT may provide complementary, non-invasive subsurface information to support individualized recall scheduling and minimally invasive repair decisions.
OriginalspracheEnglisch
ZeitschriftBioengineering
Jahrgang13
Ausgabenummer7
DOIs
PublikationsstatusVeröffentlicht - 2026

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 9 – Industrie, Innovation und Infrastruktur
    SDG 9 – Industrie, Innovation und Infrastruktur

Fingerprint

Untersuchen Sie die Forschungsthemen von „Prediction of Subsurface Fatigue Damage in Dental CAD/CAM Restorations: Intraoral Scanning vs. Optical Coherence Tomography“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren