Effect of surface finish and resin cement on the bond strength to CAD-CAM ceramics for interim resin-bonded prostheses.

STATEMENT OF PROBLEM: Resin-bonded prostheses, including interim resin-bonded prostheses, are effective in preserving tooth structure compared with other types of fixed dental prostheses for the replacement of missing teeth. However, loss of retention remains a notable concern with these types of prostheses. PURPOSE: The purpose of this in vitro study was to investigate the influence of glass-ceramic type, resin type, and surface finish on the shear bond strength (SBS) to the CAD-CAM ceramics used to fabricate interim resin-bonded prostheses. MATERIAL AND METHODS: Eighty 10×2-mm glass-ceramic disks were fabricated by using a diamond saw (IsoMet 1000), 40 from feldspathic porcelain blocks (Vita Mark II) and 40 from lithium disilicate blocks (IPS e.max CAD). Half of the specimens in each group were left with a dull or matte surface finish after cutting, while the other half were glazed with an add-on glaze (VitaAkzento Plus Glaze Spray and IPS e.max CAD Glaze Spray, respectively). The disks were mounted in acrylic resin, and each group was subdivided into 2, with 1 receiving a photopolymerized resin cement (RelyX Veneer) and the other receiving a flowable composite resin (Filtek Supreme Ultimate Flow) to form 2.38×2-mm cylinders. SBS was determined using a universal testing machine (Instron 4411) in accordance with the International Organization for Standardization (ISO) 29022:2013 standard, and failure modes were analyzed by using a stereomicroscope with ×40 magnification. The data were analyzed with a 3-way analysis of variance and Tukey post hoc analysis. The chi-squared test was used to analyze the failure mode (α=.05 for all tests). RESULTS: Ceramic type, resin type, and surface finish significantly impacted SBS (P<.001, P=.003, P<.001, respectively). Lithium disilicate showed higher SBS than feldspathic porcelain, and flowable composite resin exhibited higher SBS than resin cement. Glazed surfaces displayed lower SBS compared with the dull or matte surfaces. The combinations among the 3 materials also impacted SBS (P=.03). In addition, the combinations between ceramic type and surface finish affected SBS (P<.001), regardless of resin cement type. No other combinations affected the SBS (P>.05). The mode of failure was different among the groups (P<.001). In comparison with all other groups, cohesive failures were most prevalent in feldspathic porcelain with a dull or matte surface finish, regardless of the resin type used. CONCLUSIONS: The SBS to glass-ceramics was influenced by ceramic material, resin cement type, and surface finish. Flowable composite resin showed higher SBS than resin cement. A dull or matte surface finish exhibited greater bond strength than a glazed surface. Lithium disilicate had higher SBS than feldspathic porcelain.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist.

OBJECTIVE: Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS: Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS: The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS: Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE: While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.

The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs.

OBJECTIVES: To determine the efficacy of a deep-learning (DL) tool in assisting dentists in detecting apical radiolucencies on periapical radiographs. METHODS: Sixty-eight intraoral periapical radiographs with CBCT-proven presence or absence of apical radiolucencies were selected to serve as the testing subset. Eight readers examined the subset, denoted the positions of apical radiolucencies, and used a 5-point confidence scale to score each radiolucency. The same subset was assessed by readers under two conditions: with and without Denti.AI DL tool predictions. For the two sessions, the performance of the readers was compared. The comparison was performed with the alternate free response receiver operating characteristic (AFROC) methodology. RESULTS: Localization of lesion accuracy (AFROC-AUC), specificity and sensitivity (by lesion) detection demonstrated improvements in the DL aided session in comparison with the unaided reading session. Subgroup performance analysis revealed an increase in sensitivity for small radiolucencies and in radiolucencies located apical to endodontically treated teeth.. CONCLUSION: The study revealed that the DL technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs. ADVANCES IN KNOWLEDGE: DL tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.

Morphometric predictors of lingual undercut depth in the posterior mandible and their relevance to immediate dental implant insertion: recommendations for CBCT / Predictores morfométricos de la profundidad de la «undecurt¼ lingual en la parte posterior de la mandíbula y su relevancia para la inserción inmediata de implantes dentales: recomendaciones para la CBCT

Deep lingual undercut (LU) is commonly encountered in the posterior mandible, and is considered a risk factor in dental implants. In this study we investigated the value of data extracted from OPGs in predicting LU depth. Such predictors might be valuable in reducing the chance of lingual plate perforation (LPP) by recommending CBCTs prior to dental implant insertion when deep LU is anticipated We aimed at assessing the following variables as potential predictors of LU depth: 1) alveolar process height (measured on OPGs), 2) alveolar process width (measured on CBCTs), and 3) the distance from apical region of dental alveoli to superior margin of IAN canal (measured on OPGs). 128 CBCTs and corresponding OPGs of posterior mandibles of 128 patients (70 females, 58 males; age range=18-87 years, mean age=45.8 years, SD=17.0 years) were used. Only dentate sites of lower first (LM1) and second molars (LM2) were considered. Four predictors of LU depth were found, the strongest was the ratio between alveolar process width (which could be assessed clinically) and alveolar process height as measured on OPGs (r=.454 at LM1 site, r=.592 at LM2 site). Predictors derived from OPG measurements might be valuable in anticipating LU depth and might be more valuable when combined with alveolar process width (which might be assessed clinically). We recommend considering the suggested predictors in assessing the need of CBCT prior to immediate dental implant insertion in posterior mandible.

El socavado lingual profundo (SLU) se encuentra comúnmente en la porción posterior de la mandíbula y es considerado un factor de riesgo en los implantes dentales. En este estudio, investigamos el valor de los datos extraídos de los OPG para predecir la profundidad del SLU. Dichos predictores podrían ser valiosos para reducir la posibilidad de perforación de la placa lingual (PPL) recomendando CBCT antes de la inserción del implante dental cuando se anticipa un SLU. El objetivo consistió en evaluar las siguientes variables como posibles predictores de profundidad de SLU: 1) altura del proceso alveolar (medida en OPG), 2) ancho del proceso alveolar (medido en CBCT) y 3) la distancia desde la región apical de los alvéolos dentales al margen superior del canal IAN (medido en OPG). Se utilizaron 128 CBCT y las OPG correspondientes de mandíbulas de 128 pacientes (70 mujeres, 58 hombres; rango de edad = 18-87 años, edad media = 45,8 años, SD = 17,0 años). Sólo se consideraron los sitios dentados de los primeros molares inferiores (LM1) y los segundos molares inferiores (LM2). Se encontraron cuatro predictores de profundidad de SLU, el más fuerte fue la relación entre el ancho del proceso alveolar (que podría evaluarse clínicamente) y la altura del proceso alveolar medida en OPG (r = 0,454 en el sitio LM1, r = 0,592 en el sitio LM2). Los predictores derivados de las mediciones de OPG podrían ser valiosos para anticipar la profundidad de SLU y podrían ser más valiosos cuando se combinan con el ancho del proceso alveolar (que podría evaluarse clínicamente). Recomendamos considerar los factores predictivos sugeridos para evaluar la necesidad de CBCT antes de la inserción inmediata del implante dental en la porción posterior de la mandíbula.