Search

The study aimed to evaluate surgical angles for tuberosity cut and linear and angular measurements for all horizontal cuts in Le Fort I osteotomy using cone-beam computed tomography (CBCT).This study included 189 CBCT scans to assess the surgical angles on 3 different sites distal to maxillary second molars relative to the midsagittal plane (MSP) and the buccal cortical plane (BCP) in relation to the descending palatine foramen for tuberosity cut. The linear and angular measurements for all horizontal cuts in Le Fort I osteotomy were also evaluated.The mean surgical angles for tuberosity cut varied from 58.90 to 95.28° and 74.85 to 100.93° according to the MSP and the BCP, respectively. For the horizontal cuts, mean lengths (angles) for posterior buccal and lateral nasal wall osteotomies were 27.44 mm (13.62°) and 33.20 mm (9.60°), respectively, and a mean length of 47.12 mm was measured for nasal septum osteotomy. Additionally, the presence of impacted third molar resulted in significantly higher mean surgical angles than those with fully erupted or without third molars (p 0.01).Overall, angles running through the buccal midpoint of the third molars relative to the MSP (76.56°) or the BCP (92.31°) might be the most appropriate in clinical practice. Furthermore, some caution seems warranted when performing tuberosity cuts with the impacted third molars.To avoid potential damage to the descending palatine artery for tuberosity cut in Le Fort I osteotomy.

Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR.A narrative review was conducted on the literature on AI algorithms in DMFR.In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping.Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future.This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.

To develop prediction models for estimating the bone-graft volume needed for sinus floor elevation (SFE) based on the augmentation site, elevation height, and sinus width using cone-beam computed tomography (CBCT).CBCT scans with a medium-to-large field-of-view with bilateral maxillary sinuses partially/entirely visible, acquired from February 2016 to October 2020, were initially screened. Ten defined regions, above the maxillary first (MMA total of 133 scans (224 sinuses) were included. Three developed prediction models, composed of the sinus width and elevation height, explained 89-91% of the variation in the bone graft volumes estimated for SFE at the MMThe proposed prediction models may enable more patient-specific estimation of the bone graft volume needed for SFE.The proposed prediction models could facilitate the preparation of an adequate amount of bone graft material and patient-clinician communication about the cost of bone graft material.

Purpose Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI). Methods Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node. Results Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm. Conclusion To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.