With the rapid development of medical technology, 3D printing technology with realistic representation can perfectly display static human anatomy, while 3D visualisation technology based on Web Graphics Library (WebGL) can promote the rigid replication characteristics of traditional teaching models and express the dynamic spatial relationship between different anatomical structures. Medical students traditionally have less cognition of ankle ligament sprains. In this study, computed tomography (CT) and magnetic resonance imaging (MRI) data of the ankle joints of volunteers were used to print models of the ankle bone, tendon, and ligament using 3D printing technology, and a real-time interactive 3D digital model of the functional ankle joint was designed using 3D visualisation based on WebGL and 2D image real-time rendering technology for interactive teaching. The utility of the 3D printing model combined with the WebGL-based 3D digital teaching model was evaluated in comparison with traditional teaching methods in 24 medical students. The results showed that the total score of students in the experimental group (mean ± SD, 79.48 ± 12.93) was significantly better than that of the control group (61.00 ± 14.94) with P < 0.05. The practical test scores of the experimental group (18.00 ± 2.70) were significantly higher than those of the control group (13.67 ± 4.96) with P < 0.05. In the satisfaction survey, the feedback questionnaire showed that the interactive teaching model of 3D printing technology combined with WebGL-based 3D visualisation technology was recognised by students in terms of quality and overall satisfaction. In addition, female students who used 3D printing combined with WebGL-based 3D visualisation technology as learning aids had a greater difference in practical test scores from the control group than male students. This study has demonstrated that the interactive teaching mode of 3D printing combined with WebGL-based 3D visualisation technology is beneficial to the teaching of medical imaging, enriching the learning experience of students, and increasing the interaction between teachers and students.
PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn’s disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which include 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Nest, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.
We propose a graph-based modulation format identification (MFI) scheme for elastic optical network (EON), which exploits the trajectory information of the adjacent received symbols to identify six commonly-used modulation formats signals. A uniform grid is constructed in the first quadrant of two-dimensional (2D) Stokes plane to capture the received symbols sequence, and then the corresponding trajectory information is converted into the adjacent matrix via graph theory. The eigenvector associated with the largest eigenvalue of the adjacent matrix is selected as the discriminated-feature of the corresponding modulation format signal. Subsequently, we employ cosine similarity algorithm to obtain the modulation format of the unknown signal by analyzing the angle between the discriminated-features of the canonical modulation formats signals and the unknown signal. Then, the effectiveness of the proposed MFI scheme is verified through 28 GBaud polarization division multiplexing (PDM)-binary phase shift keying (BPSK), PDMquadrature phase shift keying (QPSK), PDM-8 quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM, and PDM-64QAM simulation systems. The simulation results show that the proposed MFI scheme achieves well performance on the required minimum optical signal-tonoise ratio (OSNR) value, the robustness to the variation of the transmission environment, residual chromatic dispersion (CD) and different group delay (DGD). Finally, the proposed MFI scheme is further verified by 20 GBaud PDM-QPSK/8QAM/-16QAM/-32QAM long-haul transmission experiments, and the results demonstrate that the proposed MFI scheme exhibits good resilience towards fiber nonlinear impairments.
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