Background/Aim: This study aimed to develop a protocol that combines cone‐beam computed tomography (CBCT), software, and 3D printing to design 3D replicas for tooth autotransplantation. The goal was to evaluate the impact of this approach on the extraoral time of the donor teeth and the total surgical time, thereby enhancing surgical efficiency and outcomes. Materials and Methods: A non‐randomized trial (protocol 10.1186/ISRCTN13563091 ) was conducted at Riga Stradins University, enrolling 46 patients (13–22 years old) who required molar extraction and possessed a non‐erupted third molar. The patients were sequentially assigned to a 3D replica group (24 patients) or a control group (22 patients). The primary outcome measured was the extra‐alveolar time of the donor tooth, and the secondary outcome was the total duration of surgery. Both were assessed using a sample size capable of detecting a 10‐min difference. A generalized linear model adjusted for various factors was used to test for significant time differences ( p < .05) between the groups. Results: Forty‐six patients were included in this analysis. The effect of using 3D replicas was not statistically significant and was associated with a decrease in the extraoral time of the donor tooth in seconds ( β = −9.35, 95% CI [−40.86, 22.16]). For the total surgical time in minutes, the use of 3D replicas had a statistically significant impact, reducing the operation duration in minutes ( β = −13.42, 95% CI [−24.50, −2.34]). No early complications were observed in either group, with all teeth present at 3–4 weeks post‐surgery. Conclusions: The integration of 3D printing technology can enhance the efficiency of autotransplantation surgeries, primarily by reducing surgical time.

In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans of the mandible. The evaluation was conducted using 188 patients’ mandibular CBCT images utilizing DCNN models built on the ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused on mandibular bone slice identification, Stage 2 pinpointed the coordinates for mandibular bone cross-sectional views, and Stage 3 computed the mandibular bone’s thickness, highlighting osteoporotic variances. The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage’s bone thickness computation algorithm reported a mean squared error of 0.8377. These findings underline the significant potential of AI in osteoporosis identification and its promise for enhanced medical care. The compartmentalized method endorses a sturdier DCNN training and heightened model transparency. Moreover, the outcomes illustrate the efficacy of a modular transfer learning method for osteoporosis detection, even when relying on limited mandibular CBCT datasets. The methodology given is accompanied by the source code available on GitLab.

Background/Aim: To evaluate the efficacy of the combined cone‐beam (CBCT)/3D‐replicas protocol on the clinical and radiographic outcomes of autotransplanted molars.Material and Methods: Controlled clinical trial registered ISRCTN13563091 from August 2019 to September 2022. Patients aged 13–22 years requiring permanent premolar extraction and having at least one non‐erupted third molar were enrolled at the Institute of Stomatology, Stradins University, Riga, Latvia. Patients in the 3D‐replicas (n = 30) underwent maxillary CBCT scans and had 3D‐printed replicas of the third molar fabricated, while the control group (n = 28) did not. The clinical outcomes included tooth mobility, bleeding on probing, and periodontal pocket depth assessed at 3, 6, and 12 months. The radiographic outcomes included root development, obliteration, periapical status, and crown changes at 12 months.Results: Of the 55 patients assigned to interventions, 46 completed the study. No significant differences in survival and radiographic outcomes were found between the control (n = 22) and 3D‐replica group (n = 24): root development (p = 0.3), root resorption (p = 0.057), periapical status (p = 0.7), and crown/root ratio change (p = 0.4). Logistic regression showed no significant associations between radiologic predictors (root resorption: p = 0.4; periapical status: p > 0.9; root development: p = 0.8). Significant clinical outcome predictors included total operative time (β = 0.0043, p = 0.049), Moorrees' stage (stage 4: β = −0.31, p < 0.001; stage 5: β = −0.39, p < 0.001), and four donor placement times (β = 0.93, p < 0.001), but group assignment was not a significant predictor.Conclusions: The CBCT/3D‐replica protocol showed no significant differences in the clinical or radiological outcomes. The high success rates in both groups suggest that the protocol is valuable primarily for optimizing surgical efficiency and as a training tool for clinicians.

Biphasic calcium phosphate ceramic granules (0.5–1.0 mm) with a hydroxyapatite and β-tricalcium phosphate ratio of 90/10 were used. Biphasic calcium phosphate ceramic granules produced in the Riga Technical University, Riga Rudolph Cimdins Biomaterials Innovation and Development Centre, were used for filling the bone loss on 18 patients with peri-implantitis. After 5 years at the minimum, clinical and 3D cone-beam computed tomography control was done. Clinical situation confirmed good stability of implants without any signs of inflammation around. Radiodensity of the previous gap and alveolar bone horizontally from middle point of dental implants showed similar radiodensity as in normal alveolar bone. This trial is registered with ISRCTN13514478.