Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.

The goal of this study was to evaluate the pH and the release of calcium from four calcium-silicate-based cements. Methods: Four materials were tested (ProClinic MTA; Angelus MTA; ProRoot MTA; Biodentine). The palatal canal root of acrylic upper molars was filled with each cement. Afterwards, they were set in phosphate-buffered saline. Measurements were taken by atomic adsorption spectroscopy (AAS) at 3, 24, 72, 168, 336, 672, and 1008 h. The pH was measured at the same timepoints. Kruskal–Wallis tests were carried out in each period, as the Kolmogorov–Smirnov and Shapiro–Wilk tests showed no parametric results. Results: Significant differences (p < 0.05) in calcium release were found at the 3-, 24-, and 72-hour evaluations. All of the analyzed groups presented a release of calcium ions up to 168 h, and the general tendency was to increase up to 672 h, with a maximum release of 25.45 mg/g in the ProRoot group. We could only observe significant differences (p < 0.05) in pH value over 168 h between the Biodentine (7.93) and Angelus MTA (7.31) groups. Conclusions: There were significant differences (p < 0.05) in calcium release. Nevertheless, no significant differences (p > 0.05) in the pH values were found at the studied timepoints, except for the values at 168 h.

To evaluate the bond strength of different universal adhesives on deciduous tooth dentine and their relationship with the composition and potential of hydrogen (pH). Methods: An in vitro micro push-out test on 150 samples (n = 50) per group per adhesive, namely, Adhese Universal (ADH; Ivoclar Vivadent), Futurabond U (FUT; Voco GmbH) (Test), and Scotchbond Universal with pre-conditioning (SCO; 3M) (Control), to record bond strength (BS) and type of adhesive failure. Results: The results of the different adhesives (megapascals (MPa)) varied, showing no statistical significance. The corresponding averages are in MPa: ADH, 13.66 2.81; FUT, 14.48 2.88; SCO, 14.98 3.96. Additionally, the frequency of type of failure was as follows: mixed (60.7%), adhesive (27.3%), and cohesive (12%). Conclusions: SCO, with a pH of 2.7, showed greater resistance to fracture, while FUT, with a pH of 2.3 and no pre-conditioning, approached the same values, being a one-step adhesive. No relationship was found between failure and type of adhesive.

Background: Dental cracks are a common yet diagnostically challenging condition due to their asymptomatic clinical nature and difficulty in early detection. Accurate diagnosis is essential to prevent crack progression and potential tooth loss; however, no universally accepted diagnostic method exists. Objectives: To systematically evaluate the diagnostic methods used for detecting dental cracks and analyze their effectiveness and limitations according to the available scientific literature. Materials and Methods: A systematic review of diagnostic test accuracy studies was conducted following the PRISMA 2020 guidelines in the PubMed and SCOPUS academic databases using the following keywords and Boolean operators (“cracked tooth” OR “cracked tooth syndrome” OR “tooth cracks” OR “dental cracks” OR “enamel cracks”) AND (diagnosis OR diagnostic OR detection OR imaging OR “diagnostic accuracy”). The search included studies published within the last 10 years, written in English, and with free full-text availability. Diagnostic test accuracy studies evaluating clinical or imaging methods for detecting dental cracks were included. Study selection, data extraction, and risk-of-bias assessment using the QUADAS-2 tool were performed independently by two reviewers. Owing to methodological heterogeneity, a qualitative synthesis of the evidence was undertaken. Results: The literature search identified 81 records, of which 12 studies met the eligibility criteria and were included in the systematic review. The evaluated diagnostic methods included visual examination, transillumination, quantitative light-induced fluorescence, cone-beam computed tomography, contrast-enhanced cone-beam computed tomography, optical coherence tomography, magnetic resonance imaging, and artificial intelligence-assisted image analysis. Considerable variability in diagnostic performance was observed among the evaluated methods. Optical coherence tomography demonstrated the highest diagnostic accuracy for detecting enamel and dentinal cracks, whereas contrast-enhanced cone-beam computed tomography improved crack detection compared with conventional cone-beam computed tomography. Conventional clinical methods remained useful as initial screening tools but showed limited diagnostic accuracy when used alone. Conclusions: No single diagnostic method demonstrated consistently high diagnostic accuracy for all types of dental cracks. Current evidence supports a multimodal diagnostic approach that combines conventional clinical examination with advanced imaging techniques when indicated to improve diagnostic reliability and facilitate treatment planning. However, the available evidence remains heterogeneous, highlighting the need for additional high-quality diagnostic test accuracy studies using standardized reference standards and reporting methods.