An Algorithm for Measuring Vertical Ground Reaction Force Based on Insole Sensors / 医用生物力学

Objective A practical and highly accurate algorithm for dynamic monitoring of plantar pressure was proposed, the magnitude of vertical ground reaction force (vGRF) during walking was measured by a capacitive insole sensor, and reliability of the prediction accuracy was verified. Methods Four healthy male subjects were require to wear capacitive insole sensors, and their fast walking and slow walking data were collected by Kistler three-dimensional (3D) force platform. The data collected by the capacitive insole sensors were pixelated, and then the processed data were fed into a residual neural network, ResNet18, to obtain high-precision vGRF. Results Compared with analysis of the data collected from Kister force platform, the normalized root mean square error (NRMSE) for fast walking and slow walking were 8.40% and 6.54%, respectively, and the Pearman correlation coefficient was larger than 0.96. Conclusions This study provides a novel algorithm for dynamic measurement of GRF in mobile scenarios, which can be used for estimation of complete GRF outside the laboratory without being constrained by the number and location of force plates. Potential application areas include gait analysis and efficient capture of pathological gaits.

MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation / 数字中医药（英文）

Objective@#For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed.@*Methods@#To obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of encoder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyramid pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation networks.@*Results@#The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.@*Conclusion@#Based on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features, which can provide another diagnosis method for computer-aided Chinese medical diagnosis.

Application of deep residual neural network in detecting small bowel obstruction by abdominal plain film / 西安交通大学学报(医学版)

Objective: To study whether a deep residual neural network can detect small bowel obstruction patterns on upright abdominal radiographs. Methods:The data of training set and test set used in this study were obtained from The First Affiliated Hospital of Xi'an Jiaotong University and No.215 Hospital of Shaanxi Nuclear Industry; the data of validation set came from No.215 Hospital of Shaanxi Nuclear Industry. Totally 3 298 clinical upright abdominal radiographs obtained from two hospitals were classified into obstructive and non-obstructive categories independently by two radiologists on the basis of the four signs on upright abdominal radiographs, who discussed and reached consensus when disagreements arose. Among them, 569(17.3%) images were found to be consistent with small bowel obstruction, and 2 729 (82.7%) images had no small bowel obstruction. A total of 2 305 training sets and 993 test sets (training set: test set = 2.3:1) were composed of data from the two groups, including 405 cases (17.6%) of small bowel obstruction, 1 900 cases (82.4%) of non-small bowel obstruction, 164 cases (16.5%) of small bowel obstruction, and 829 cases (83.5%) of non-small bowel obstruction. The diagnosis of small bowel obstruction in training and testing sets was based on experienced radiologists' evaluation. Totally 861 abdominal upright abdominal radiographs constituted the validation set (99 with small bowel obstruction and 762 with no small bowel obstruction); the surgical results and clinical diagnosis were set as the gold standard. In this study, the image 2012 large-scale visual recognition challenge data set (ILSVRC2012) was used for pre-training the deep residual neural network (ResNet38). The retraining of deep residual network (ResNet38) with training set data was used to establish the diagnostic model. The test set was mainly used in the learning algorithm process to adjust the algorithm parameters to modify the network, so as to make the network model more efficient. Results: After training, the deep residual neural network achieved an AUC of 0.83 on the test set (95% CI 0.82-0.92). The sensitivity of the system for small bowel obstruction was 84.1%, with a specificity of 65.0%. And on validation set it achieved an AUC of 0.87 (95% CI 0.82-0.92), the sensitivity of the system for small bowel obstruction was 89.9%, with a specificity of 68.0%. Conclusion: Transfer learning with deep residual neural network may be used to train a detector for small bowel obstruction on upright abdominal radiographs even with limited training data.