Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography.

OBJECTIVES: This study aimed to determine mastoid emissary canal's (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. METHODS: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. RESULTS: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. CONCLUSION: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique.

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model's performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.

Automatic Feature Segmentation in Dental Periapical Radiographs.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

Does serum hexosaminidase activity play a role in the diagnosis of strangulated bowel obstruction? An experimental study.

Strangulation is associated with an increased risk of mortality and morbidity in patients with mechanical bowel obstruction. The accurate and early recognition of the presence of strangulation is important to allow safe nonoperative treatment. A number of studies have shown that there was no single and reliable test to detect or exclude the presence of strangulation. The aim of this study was to evaluate the role of serum hexosaminidase (Hex) levels in recognition of strangulation in an experimental model of closed loop small bowel obstruction. Forty-two Wistar albino rats were divided into four groups: I, control (n = 5); II, sham laparotomy (n = 5); III, simple obstruction (n = 16); and IV, strangulation groups (n = 16). Activity levels of total Hex and its fractions (Hex A and B) were assayed in serum samples obtained from rats after 3 and 8 hr. Samples of small bowel were also evaluated histologically. Histological evaluation of bowel sections obtained from the strangulation group after 8 hr, revealed transmural hemorrhagic infarction in all animals with a mean +/- SD total Hex activity of 978.25 +/- 150 nmol/hr/ml, which was significantly higher than that in the other groups (P < 0.001). Although sections of bowel from the strangulation group after 3 hr showed severe ischemic injury, the activities of total Hex, Hex A, and Hex B were not different from those of the control, sham, and simple obstruction groups. Histological examination of these groups did not show any sign of ischemia. Total Hex, Hex A, and Hex B activities in the strangulation group were all significantly greater than the activities seen in the simple obstruction group (P < 0.001, for all). In conclusion, increased serum hex levels indicate irreversible transmural infarction only in the late period of strangulation in the closed loop small bowel obstruction model. It seems unuseful for detecting reversible and/or irreversible ischemia in the early period of strangulation.