OBJECTIVE: The purpose of this pilot study was to present Ethoshunt as a gamification-based mobile app that can be used in teaching and learning ethics.
METHODS: This study involved a mixed-methods research design. The researchers surveyed 39 undergraduate students who were introduced to Ethoshunt in order to examine the relationships between mobile app usability and positive emotions, ethical competency, and user experience. Affinity diagramming was used as a tool to organize the opinions and experiences of participants using featured gamification elements.
RESULTS: Game dynamics and game mechanics explained the functionality of Ethoshunt. In addition, the learning flow through Ethoshunt was discussed. Overall, the findings were positive, and mobile app usability had the strongest relationship with positive emotions (r=0.744, P
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
METHODS: This is a prospective cross-sectional study of asymptomatic type 2 diabetics selected from the outpatient ophthalmology and endocrine clinics for carotid duplex ultrasound scanning performed by a single radiologist. The duplex ultrasound criteria were based on the North American Symptomatic Carotid Endarterectomy Trial (NASCET) classification of carotid artery stenosis. Univariate and multivariate analysis was performed to identify possible risk factors of carotid artery stenosis.
RESULTS: Amongst the 200 patients, the majority were males (56%) and Malay predominance (58.5%). There were 12/200 patients (6%) with mean age of 69.2 years identified to have carotid artery stenosis. Univariate analysis of patients with asymptomatic carotid artery stenosis identified older age of 69.2 years (p=0.027) and duration of exposure to diabetes of 17.9 years (p=0.024) as significant risk factors.
CONCLUSION: Patients with longer exposure of diabetes and older age were risk factors of carotid artery stenosis in asymptomatic type 2 diabetics. These patients should be considered for selective screening of carotid artery stenosis during primary care visit for early identification and closer surveillance for stroke prevention.