Background: Nowadays, a combination of classical subperiosteal implant designs with 3D imaging and printing allows one to reduce treatment time and provides support for fixed prostheses in cases where other techniques do not provide satisfactory results. This study aims to present a digital technique for the manufacturing of custom-made subperiosteal implants and what complications might appear after this type of surgery. Methods: Sixteen patients treated with a custom-made DMLS titanium subperiosteal implant during the period between October 2021 and February 2022 were enrolled in the study. Orthopantomography (OPT) and cone-beam computer tomography (CBCT) were recorded for all patients. The measurements taken into account in this study were the fit and stability of implants, duration of surgery, implant survival, and early and late complications. Results: The fit of the implants was extremely satisfactory, with a mean rating of 4 out of 5. The mean duration of the intervention was 86.18 min. At the end of the study, one implant was lost due to insufficient fit and recurrent, untreatable infections. Eleven implants (69%) were placed on the maxillary and five (31%) implants were placed on the mandible. Conclusions: Taking this into consideration, custom-made DMLS titanium subperiosteal implants could present satisfactory implant survival and low complication rates.

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. Backgrounds and Objectives: This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. Material and Methods: A comparative study was conducted involving 60 practitioners divided into three groups—general dentists, specialists, and senior specialists—along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference “gold standard” validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. Results: AI’s consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, p = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. Conclusions: AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.

Background and Objectives: Odontogenic sinusitis is a frequently underestimated pathology with fewer symptoms in patients with periapical lesions, periodontal disease, or iatrogenic foreign bodies in the maxillary sinus. The aim of our study was to determine the correlation between maxillary sinusitis and periapical lesions using cone-beam computed tomography (CBCT) imaging and histological and immunohistochemical investigations. Materials and Methods: A total of 1450 initial patients diagnosed with maxillary sinusitis in the Ear-Nose-Throat (ENT) Department, University of Medicine and Pharmacy “Grigore T. Popa” Iasi, Romania, were treated with anti-inflammatory drugs. Of these, 629 still had unresolved symptomatology and were later referred to the Dental Medicine departments for further investigations. Only 50 subjects with periapical lesions in the premolar/molar maxillary area were included in the present study. All the periapical lesions were observed on CBCT and classified using the Periapical Status Index (PSI) and the mean maxillary sinus mucosa thicknesses (MSMT). The enrolled patients underwent surgical procedures with the excision of periapical lesions. The excised samples were submitted to the histological and immunohistochemical investigations. Results: The 50 patients presented periapical lesions of their maxillary teeth in 328 dental units. There was a higher prevalence of periapical lesions in men than in women (chi-square test). We observed a significant difference between the mean MSMT of individuals with periapical lesions compared to those without (p < 0.01). Mean MSMT was 1.23 mm for teeth without periapical lesions and 3.95 mm for teeth with periapical lesions. The histopathological study identified 50% cases with periapical granulomas, 10% cases with periapical granulomas with cystic potential, and 40% cases as periapical cysts. Immunohistochemical stainings showed that CD4+ helper and CD8+ cytotoxic T lymphocytes, along with CD20+ B lymphocytes and CD68+ macrophages, were diffusely distributed in all periapical cysts and in some periapical granulomas, but CD79α+ plasma cells characterized especially periapical granulomas. Conclusions: The current study observed a significant correlation between CBCT maxillary mucosa thickness and type of periapical lesion. Chronic inflammatory lympho-histiocytic infiltrate predominates in periapical lesions, supporting the idea that lesion progression is determined by a humoral-type (CD20+ and CD79α+ B lymphocytes) but also by a cellular-type (CD4+ and CD8+ T lymphocyte population) immune mechanism.

Gardner syndrome is a neoplasic disease that associates intestinal polyposis and colorectal adenocarcinoma with osteomas and soft tissue tumors determined by germline mutations in the APC gene. The early diagnosis and identification of high-risk individuals are important because patients have a 100% risk of colon cancer. We present the case of a family with Gardner syndrome. Cephalometric, panoramic X-rays and CBCT of the proband and her brother showed multiple osteomas affecting the skull bones, mandible and paranasal sinuses. The detailed family history showed an autosomal dominant transmission with the presence of the disease in the mother and maternal grandfather of the proband. Both had the typical signs of disease and died in the fourth decade of life. Based on these aspects the clinical diagnosis was Gardner syndrome. By gene sequencing, a novel pathogenic variant c.4609dup (p.Thr1537Asnfs*7) in heterozygous status was identified in the APC gene in both siblings. We reviewed literature data concerning the correlation between the localization of mutations in the APC gene and the extracolonic manifestations of familial adenomatous polyposis as well as their importance in early diagnosis and adequate oncological survey of patients and families based on abnormal genomic variants.

Many developments were made in the area of endovascular treatment of intracranial aneurysms, but this procedure also requires a good assessment of vascular anatomy prior to intervention. Seventy-six cases with brain aneurysms were selected and 1:1 scale 3D printed models were created. We asked three interventional neurosurgeons with different degrees of experience (ten years, four years, and a fourth-year resident) to review the cases using CTA (computed tomography angiogram) with MPR (multiplanar reconstructions) and VRT (volume rendering technique) and make a decision: coil embolization or stent-assisted coil embolization. After we provided them with the 3D printed models, they were asked to review their treatment plan. Statistical analysis was performed and the endovascular approach changed in 11.84% of cases for ten-year experienced neurosurgeons, 13.15% for four years experienced neurosurgeon, and 21.05% for residents. The interobserver agreement was very good between the ten years experienced interventionist and four years experienced interventionist when they analyzed the data set that included the 3D printed model. The agreement was higher between all physicians after they examined the printed model. 3D patient-specific printed models may be useful in choosing between two different endovascular techniques and also help the residents to better understand the vascular anatomy and the overall procedure.

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.