Bioactive glass ionomer cements (GICs) have been in widespread use for ∼40 years in dentistry and medicine. However, these composites fall short of the toughness needed for permanent implants. Significant impediment to improvement has been the requisite use of conventional destructive mechanical testing, which is necessarily retrospective. Here we show quantitatively, through the novel use of calorimetry, terahertz (THz) spectroscopy and neutron scattering, how GIC’s developing fracture toughness during setting is related to interfacial THz dynamics, changing atomic cohesion and fluctuating interfacial configurations. Contrary to convention, we find setting is non-monotonic, characterized by abrupt features not previously detected, including a glass–polymer coupling point, an early setting point, where decreasing toughness unexpectedly recovers, followed by stress-induced weakening of interfaces. Subsequently, toughness declines asymptotically to long-term fracture test values. We expect the insight afforded by these in situ non-destructive techniques will assist in raising understanding of the setting mechanisms and associated dynamics of cementitious materials.

Cemento-osseous dysplasia is a benign fibro-osseous lesion of the tooth-bearing region of the jaws with a periodontal ligament origin. It appears predominantly in Black and Asian middle-aged females. Its importance is that it could mimic a periapical lesion in the early, translucent stage. In this report a rare case of familial cemento-osseous dysplasia is presented: a 50-years old Caucasian woman with labial paraesthesia and radiological translucency around the roots of the mandibular incisors and the first molar teeth. The lesion around the first molar was diagnosed as periapical granuloma and a root canal treatment was carried out. The diagnosis of florid cemento-osseous dysplasia and the treatment plan based on two- and three-dimensional radiographic examinations were certified histologically after surgical removal of the lesion. We screened the family members - including the patient’s mother, daughter and son - and identified a periapical version of cemento-osseous dysplasia in the daughter. Our case highlights the difficulties of differential diagnosis of cemento-osseous dysplasia and other periapical pathologies. The inconsistencies in the present classification of cemento-osseous dysplasia are also discussed with a proposal for a different classification based on new aspects that would be very helpful in setting up a correct treatment plan.

Background/Objectives: Our study aimed to determine the accuracy of the artificial intelligence-based Diagnocat system (DC) in detecting periapical lesions (PL) on panoramic radiographs (PRs). Methods: 616 teeth were selected from 357 panoramic radiographs, including 308 teeth with clearly visible periapical radiolucency and 308 without any periapical lesion. Three groups were generated: teeth with radiographic signs of caries (Group 1), teeth with coronal restoration (Group 2), and teeth with root canal filling (Group 3). The PRs were uploaded to the Diagnocat system for evaluation. The performance of the convolutional neural network in detecting PLs was assessed by its sensitivity, specificity, and positive and negative predictive values, as well as the diagnostic accuracy value. We investigated the possible effect of the palatoglossal air space (PGAS) on the evaluation of the AI tool. Results: DC identified periapical lesions in 240 (77.9%) cases out of the 308 teeth with PL and detected no PL in 68 (22.1%) teeth with PL. The AI-based system detected no PL in any of the groups without PL. The overall sensitivity, specificity, and diagnostic accuracy of DC were 0.78, 1.00, and 0.89, respectively. Considering these parameters for each group, Group 2 showed the highest values at 0.84, 1.00, and 0.95, respectively. Fisher’s Exact test showed that PGAS does not significantly affect (p = 1) the detection of PL in the upper teeth. The AI-based system showed lower probability values for detecting PL in the case of central incisors, wisdom teeth, and canines. The sensitivity and diagnostic accuracy of DC for detecting PL on canines showed lower values at 0.27 and 0.64, respectively. Conclusions: The CNN-based Diagnocat system can support the diagnosis of PL on PRs and serves as a decision-support tool during radiographic assessments.