Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia.

OBJECTIVES: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia. METHODS: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification. RESULTS: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000. CONCLUSION: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia. KEY POINTS: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

Evaluating the effectiveness of a virtual reality simulation for preclinical local anaesthesia dental education.

INTRODUCTION: Traditional manikin training has limitations that virtual reality can address. This study investigated the effectiveness of two part-task training simulation methods, a virtual reality (VR Sim) vs a plastic manikin (PM Sim), on learning outcomes for local anaesthesia skills for second-year pre-clinical dental students. METHODS: In an experimental study, 58 second-year students were randomly assigned to one of two groups, VR Sim or PM Sim. Both groups completed the same pre-post survey. The VR Sim group practiced with a VR simulation, completed a built-in treatment test and a transfer test with a live person, and was evaluated by an expert teaching assistant (TA) with a rubric. The PM Sim group practiced with a plastic manikin and completed a treatment test on the same manikin evaluated by a TA, followed by the same transfer test with a live person and evaluated by a TA with a rubric. RESULTS: Covering knowledge and skills in the delivery of local anaesthesia, mean final transfer test scores were statistically significantly higher for the PM Sim compared to VR Sim, F(1, 57) = 9.719, p = .003 with effect size, η2 p  = 0.148. Scores on respective treatment tests were similar to final transfer test scores for each group suggesting differences were localised to the practice methods. Pre-survey results indicated participants had low prior experience with VR technology. CONCLUSION: Whilst outcomes showed higher results for plastic manikin tutor training over the VR training method, they are complementary. As students practice more with the technology and the VR simulation they may improve further. Likewise, as the technology for haptics with VR improves beyond hand controllers so may the experience and learning of this skill for students.

Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope.

Objective: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images. Methods: From 54 VL videos, clinicians manually labeled images that include motion blur, foggy vision, blood, mucus, and vomitus. Anatomical structures of interest included the tongue, epiglottis, vocal cord, and corniculate cartilage. EfficientNet-B5 with DeepLabv3+, EffecientNet-B5 with U-Net, and Configured Mask R-Convolution Neural Network (CNN) were used; EffecientNet-B5 was pretrained on ImageNet. Dice similarity coefficient (DSC) was used to measure the segmentation performance of the model. Accuracy, recall, specificity, and F1 score were used to evaluate the model's performance in targeting the structure from the value of the intersection over union between the ground truth and prediction mask. Results: The DSC of tongue, epiglottis, vocal cord, and corniculate cartilage obtained from the EfficientNet-B5 with DeepLabv3+, EfficientNet-B5 with U-Net, and Configured Mask R-CNN model were 0.3351/0.7675/0.766/0.6539, 0.0/0.7581/0.7395/0.6906, and 0.1167/0.7677/0.7207/0.57, respectively. Furthermore, the processing speeds (frames per second) of the three models stood at 3, 24, and 32, respectively. Conclusions: The algorithm developed in this study can assist medical providers performing ETI in emergent situations.