MATERIALS AND METHODS: The courses of the mandibular canal in 202 cone-beam computed tomography scanned images of healthy Malaysians were evaluated, and trifid mandibular canal (TMC) when present, were recorded and studied in detail by categorizing them to a new classification (comprising of 12 types). The diameter and length of canals were also measured, and their shape determined.

RESULTS: Trifid mandibular canals were observed in 12 (5.9%) subjects or 16 (4.0%) hemi-mandibles. There were 10 obvious categories out the 12 types of TMCs listed. All TMCs (except one) were observed in patients older than 30 years. The prevalence according to ethnicity was 6 in Malays, 5 in Chinese and 1 in Indian. Four (33.3%) patients had bilateral TMCs, which was not seen in the Indian subject. More than half (56.3%) of the accessory canals were located above the main mandibular canal. Their mean diameter was 1.32 mm and 1.26 mm for the first and second accessory canal, and the corresponding lengths were 20.42 mm and 21.60 mm, respectively. Most (62.5%) canals had irregularly shaped lumen; there were more irregularly shaped canals in the second accessory canal than the first branch. None of the second accessory canal was oval (in shape).

METHODS: External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance.

RESULTS: RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs.

MATERIALS AND METHODS: In this study, 20 implant sites in patients were selected. Ridge mapping was done through a vacuum press template at three buccal (B1, B2, B3), three lingual (L1, L2, L3), and one crestal (C) points for each implant site. Readings were transferred onto the cast, and surgical guides were fabricated for implant placement. Postoperative cone beam computerized tomography (CBCT) was done to assess planned and achieved implant position. Comparison was done between soft tissue depths and implant distance from the crest of alveolar bone determined by the ridge mapping technique with measurements done on CBCT. The points used for ridge mapping were used as the reference for measurements. The data were analyzed using paired t test. p < 0.05 was considered to be statistically significant.

RESULTS: On comparing the mean values of soft tissue depths from the ridge mapping and CBCT data, insignificant differences were found at B1, B2, L1, L2, L3, and C, but significant differences were found at B3. On comparing the implant distances from alveolar bone from both the data, insignificant differences were found at B, B2, B3, L1, L2, and L3 and significant difference was found at the crest in the mean values.

METHODS: CBCT was used to assess 200 joints in 100 subjects (mean age, 30.5 years). i-CAT CBCT software and The Mimics 16.0 software were employed to measure the volume, metrical size, position of each condyle sample and the thickness of the roof of the glenoid fossa (RGF).

RESULTS: No significant gender differences were noted in thickness of the RGF and condylar length; however condylar volume, width, height and the joint spaces were significantly greater among males. With regards to comparison of both TMJs, the means of condylar volume, width and length of the right TMJ were significantly higher, while the means of the left condylar height and thickness of RGF were higher. When comparing the condylar measurements and the thickness of RGF between the two ethnic groups, we found no significant difference for all measurements with exception of condylar height, which is higher among Chinese.

METHODS: CBCT images were scanned retrospectively and the ones including bilateral M1Ms were included in the study. The evaluation was performed by 1 researcher in each country, trained with CBCT technology. A written and video instruction program explaining the protocol to be followed step-by-step was provided to all observers to calibrate them. The CBCT imaging screening procedure consisted of evaluating axial sections from coronal to apical. The presence of DLC and RE in M1Ms (yes/no) was identified and recorded.

RESULTS: Six thousand three hundred four CBCTs, representing 12,608 M1Ms, were evaluated. A significant difference was found between countries regarding the prevalence of both RE and DLC (P  .05).

METHODS: A ball phantom was scanned using panoramic mode of the Planmeca ProMax 3D Mid CBCT unit (Planmeca, Helsinki, Finland) with standard exposure settings used in clinical practice (60 kV, 2 mA, and maximum FOV). An automated calculator algorithm was developed in MATLAB platform. Two parameters associated with panoramic image distortion such as balls diameter and distance between middle and tenth balls were measured. These automated measurements were compared with manual measurement using the Planmeca Romexis and ImageJ software.