The implementation of CAD-CAM systems in dentistry has significantly influenced the evolution of dental implantology and implant-supported prosthetics within the past three decades. Implant-supported prostheses are comfortable and aesthetic. The prosthetic abutment has also faced a rapid design evolution, from the individualization of standard stock abutments offered by various manufacturers to a modern customization process using CAD-CAM technology. This paper presents a comparative study between 20 dental custom CAD-CAM implant abutments and 20 dental implant stock abutments, based on a set of measurements performed on the digital casts obtained from 24 cases of prosthetic rehabilitation on implants. The statistical analysis (Mann–Whitney U test) revealed significant differences between these two types of abutments: the incisal margin line diameter dimensions for custom abutments were significantly improved compared to standard abutments at the cervical level (U = 343.00, z = 3.868, p < 0.0005) and the incisal/occlusal level (U = 352.00, z = 4.112, p < 0.0005), while the inclination angle of the custom abutments relative to the 0-axis was significantly smaller than that of standard abutments (U = 115.50, z = −2.286, p = 0.022). The use of custom abutments leads to an increase in the final size of the abutment, an improvement in the retention of the prosthetic work, and reduces the angulation of the abutment in relation to the implant axis, thus decreasing the risk of unscrewing or fracturing the dental screw.

Background: Developmental defects of enamel (DDE) are frequently encountered in primary and permanent teeth, yet their etiology is not completely known. Enamel hypoplasia is considered a predisposing factor for early caries. The objective of this study was the evaluation of several risk factors potentially causing DDE and the possible association between DDE and dental caries. Methods: This study was performed on a group of 213 rural children from Romania. It combined a thorough dental examination for all children, and a questionnaire filled in by their mothers, regarding the evolution of their pregnancy and the child’s health status in the first years of life. Results: There was no statistically significant association between DDE presence and data regarding the evolution of pregnancy, mothers’ health status or children’s conditions during early childhood. There was a significant association between the use of amoxicillin, ibuprofen, and cephalosporin during the period of formation of permanent teeth, and one environmental factor (water source), and the presence of DDE (Chi Square, p < 0.05). Also, DDEs were associated with the presence of caries (Fisher, p = 0.001). Conclusions: Children who consumed water from private wells and children who received medication during early childhood developed more enamel defects, presenting a higher risk of caries development.

Background: Implant-prosthetic therapy requires a detailed assessment of the bone structure before designing a personalized treatment plan. Tooth extraction at the molar level is followed by a series of bone changes dependent on the patient’s general condition and age and the area in which it was performed. The fractal analysis of cone beam computed tomography (CBCT) represents a way to assess the quality of post-extraction bone regeneration. The purpose of this study was to analyze the alveolar bone changes after tooth extraction using fractal analysis on CBCT images. Methods: This retrospective study included pre- and post-extraction CBCTs at 3 months of 60 patients who underwent 100 extractions of first and/or second molars. Fractal analysis on CBCT images was performed using ImageJ, and the data obtained from the measurements were statistically processed. A multiple regression model was used to assess factors influencing bone remodeling. Results: Fractal analysis performed on CBCT images showed that most patients experienced advanced bone remodeling, this being more pronounced in those from rural areas, in the vertical plane at the mandible and at the second molar. The multiple regression model showed that the factors that play an important role in predicting bone resorption are represented by age group (age above 56 years old is associated with less bone resorption), location (bone resorption is more pronounced at the mandible level), and molar (bone resorption for the second molar is higher). Conclusions: Post-extraction bone changes were influenced by the age of the patient and by the location of the extraction, with the maxilla and first molar having better fractal analysis values compared to the mandible and second molar. These results emphasize the importance of training implantologists in CBCT evaluation to improve personalized implant-prosthetic treatment decisions.

Background/Objectives: Saliva plays an essential role in maintaining the oral ecological balance, and its quantitative and qualitative characteristics may influence susceptibility to dental caries. The aim of this study was to determine susceptibility to dental caries based on the DMFT index and to establish a correlation between caries experience and salivary parameters in a group of young adults. Methods: This cross-sectional study was conducted between July and November 2025 on a sample of 87 fourth-year students from the Faculty of Dentistry in Craiova. Each participant underwent an intraoral clinical examination to determine the DMFT index. The salivary parameters assessed included unstimulated salivary flow rate, saliva consistency, salivary pH, stimulated salivary flow rate, and buffering capacity, using the GC Saliva-Check Buffer kit. Statistical analyses were performed using SPSS (Statistical Package for Social Sciences) software, version 26 (SPSS Inc., Armonk, NY, USA). Results: The mean DMFT index value for the entire sample was 8.26 ± 4.481, with higher values observed among female participants. Low salivary pH was significantly associated with higher DMFT values. Participants with low or very low buffering capacity exhibited higher DMFT values compared to those with normal capacity, indicating that a reduced ability to neutralize salivary acidity is associated with increased caries activity. Conclusions: The results indicate that salivary pH and buffering capacity are important factors in dental caries susceptibility among young adults. The integration of salivary testing into the diagnostic assessment of caries risk may contribute to personalized and effective preventive strategies.

Recently, due to the high demand for dental implants, the use of dental implant stock abutments has increased significantly, especially dental custom CAD/CAM implant abutments milled by dental technicians in their laboratories. The purpose of this study is to analyze the surface quality of the emergence profile of dental custom CAD/CAM implant abutments made by a non-industrial milling machine, compared to original and compatible dental implant stock abutments made by industrial machines. Thirty dental implant abutments were divided into six study lots. Lot 1 (control group): original dental implant stock abutments—industrial machined; lot 2 (study group): compatible dental implant stock abutments—industrial machined; lots 3, 4, 5, and 6 (study groups): compatible custom CAD/CAM dental implant abutments—non-industrial milled with hyperDENT CAM software and Paragon Tools. The Nikon SMZ745T stereomicroscope was used to analyze the emergence profile surface of each dental implant abutment. The structure of the analyzed surfaces did not show significant differences between original and compatible abutments that were industrially machined. As for the customized dental implant abutments, the greatest similarity with the original was obtained for lot 6, and a significant statistical difference was obtained for lot 4. Stepover and Feed Rate parameters of the milling process influenced the surface roughness of the emergence profile for the customized dental implant abutments. The digital technology of non-industrial milling compatible custom CAD/CAM dental implant abutments is reliable and within the correct milling parameters.

Artificial intelligence (AI) is rapidly transforming the medical oncology, by providing new tools for early diagnosis and personalized treatment planning. In mandibular oncology, where malignant transformation and metastatic progression can drastically alter prognosis, AI-driven systems show great promise in enhancing clinical decision-making. Convolutional neural networks and deep learning algorithms have already demonstrated high accuracy in analyzing imaging datasets, including CT scans and histopathological slides, for the detection of subtle changes that may indicate malignant transformation of initially benign lesions. These tools also aid in identifying early metastatic spread, allowing for timely intervention. One of the most aggressive head and neck cancers, oral squamous cell carcinoma (OSCC) usually affects the mandible and causes extensive anatomical disruption, neurological impairments, and functional deficits in speech, mastication, and facial symmetry. This article presents clinical and histopathological data on malignant lesions of the mandibular bone, primarily oral squamous cell carcinoma (OSCC). Tumours primarily affected the mandibular body and ramus, often causing facial asymmetry, tooth mobility, and bone loss. Histologically, keratin pearls and spindle-shaped atypical cells were typical characteristics. Neurological involvement from perineural invasion frequently led to pain, numbness, and impaired oral function. In addition to the complex consequences of mandibular cancers, such as their anatomical, functional, and psychological effects, the study underscores the importance of multidisciplinary treatment and rehabilitation. Our paper presents how new methods for improving the precision and effectiveness of oral oncology diagnosis have been made possible by developments in artificial intelligence (AI), especially deep learning approaches. By recognising both cellular and structural atypia, convolutional neural networks (CNNs) have shown excellent performance in the classification of histopathological images. The distinction between benign, dysplastic, and malignant oral lesions is further enhanced by hybrid techniques that blend deep and texture-based features. AI-assisted radiomics has shown promise in diagnostic imaging by detecting high-dimensional, subtle characteristics in CT, MRI, and ultrasound scans that are frequently impossible to interpret visually. According to analysed studies, these techniques can improve the identification of lymph node metastases and offer accurate risk assessment for oral malignancies. Further encouraging their inclusion into the oral oncology workflow, machine learning models have attained diagnostic accuracies that surpass standard radiological assessments by merging image texture analysis with clinical data. A triadic viewpoint is provided by integrating AI into mandibular oncology: (1) imaging-based tumour growth and metastasis detection, (2) histopathological evaluation of malignant potential, and (3) assessment of neurological outcomes following surgery. Also, this article presents a case study from the University of Craiova, guide surgical excision, and assess both histological progression and postoperative functional impact. AI technologies' ability to support individualized treatment plans, enable earlier diagnoses, and enhance clinical judgment is becoming more and more clear as they develop—especially in anatomically and neurologically complex areas like the mandible. In conclusion, AI represents a powerful ally in mandibular oncology, bridging imaging, histopathology, and neurological evaluation, and paving the way for more precise, patient-centered care.