Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
ObjectiveTo apply the multi-modal deep learning model to automatically classify the ultra-widefield fluorescein angiography (UWFA) images of diabetic retinopathy (DR). MethodsA retrospective study. From 2015 to 2020, 798 images of 297 DR patients with 399 eyes who were admitted to Eye Center of Renmin Hospital of Wuhan University and were examined by UWFA were used as the training set and test set of the model. Among them, 119, 171, and 109 eyes had no retinopathy, non-proliferative DR (NPDR), and proliferative DR (PDR), respectively. Localization and assessment of fluorescein leakage and non-perfusion regions in early and late orthotopic images of UWFA in DR-affected eyes by jointly optimizing CycleGAN and a convolutional neural network (CNN) classifier, an image-level supervised deep learning model. The abnormal images with lesions were converted into normal images with lesions removed using the improved CycleGAN, and the difference images containing the lesion areas were obtained; the difference images were classified by the CNN classifier to obtain the prediction results. A five-fold cross-test was used to evaluate the classification accuracy of the model. Quantitative analysis of the marker area displayed by the differential images was performed to observe the correlation between the ischemia index and leakage index and the severity of DR. ResultsThe generated fake normal image basically removed all the lesion areas while retaining the normal vascular structure; the difference images intuitively revealed the distribution of biomarkers; the heat icon showed the leakage area, and the location was basically the same as the lesion area in the original image. The results of the five-fold cross-check showed that the average classification accuracy of the model was 0.983. Further quantitative analysis of the marker area showed that the ischemia index and leakage index were significantly positively correlated with the severity of DR (β=6.088, 10.850; P＜0.001). ConclusionThe constructed multimodal joint optimization model can accurately classify NPDR and PDR and precisely locate potential biomarkers.
The classification of thymoma has always been controversial topil in recent years. It hasn’t been unified because of the morphological diversity of thymoma, the heterogeneity of tumour cells and the lack of simple and effective observation index. With the development of diagnostic technique and oncobiology research, several classification methods have been drawn off, including its World Health Organization(WHO) lassification. We reviewed the main classification and discussed the problems of each classification method and their clinical guiding significamce, summarized the development tendency, methods assist the classification and clinical research of thymoma.
Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.
This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
In recent years, more and more new diagnostic and examining techniques are popularized, which improves the level of ocular fundus disease diagnosis and treatment. Because of the uneven distribution of diagnostic and therapeutic instruments resources, low level of application techniques, and different professional level of the doctors, the improvement and development of the application of the equipments and level of the diagnosis and treatment were inhibited. Appropriate understanding and applying basic diagnostic techniques of ocular fundus disease and comprehensive promoting the professionalsprime; levels are an urgent problem needs to be solved.
Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.
Subtype classification of age-related macular degeneration (AMD) based on optical coherence tomography (OCT) images serves as an effective auxiliary tool for clinicians in diagnosing disease progression and formulating treatment plans. To improve the classification accuracy of AMD subtypes, this study proposes a keypoint-based, attention-enhanced residual network (KPA-ResNet). The proposed architecture adopts a 50-layer residual network (ResNet-50) as the backbone, preceded by a keypoint localization module based on heatmap regression to outline critical lesion regions. A two-dimensional relative self-attention mechanism is incorporated into convolutional layers to enhance the representation of key lesion areas. Furthermore, the network depth is appropriately increased and an improved residual module, ConvNeXt, is introduced to enable comprehensive extraction of high-dimensional features and enrich the detail of lesion boundary contours, ultimately achieving higher classification accuracy of AMD subtypes. Experimental results demonstrate that KPA-ResNet achieves significant improvements in overall classification accuracy compared with conventional convolutional neural networks. Specifically, for the wet AMD subtypes, the classification accuracies for inactive choroidal neovascularization (CNV) and active CNV reach 92.8% and 95.2%, respectively, representing substantial improvement over ResNet-50. These findings validate the superior performance of KPA-ResNet in AMD subtype classification tasks. This work provides a high-accuracy, generalizable network architecture for OCT-based AMD subtype classification and offers new insights into integrating attention mechanisms with convolutional neural networks in ophthalmic image analysis.
ObjectiveTo investigate the effectiveness of fixation the posterior malleolus or not to treat different Haraguchi’s classification of posterior malleolus fractures.MethodsThe clinical data of 86 trimalleolar fracture patients who were admitted between January 2015 and September 2019 and met the selection criteria were retrospectively reviewed. There were 29 males and 57 females; the age ranged from 26 to 82 years with a mean age of 55.2 years. According to Haraguchi’s classification, 38 patients were in type Ⅰ group, 30 patients in type Ⅱ group, and 18 patients in type Ⅲ group. There was no significant difference in the general data such as gender, age, and fracture location among the 3 groups (P>0.05). The fixation of the posterior malleolus was performed in 23, 21, and 5 patients in type Ⅰ, Ⅱ, and Ⅲ groups, respectively. The operation time, fracture healing time, full weight-bearing time, postoperative joint flatness, and joint degeneration degree of the patients in each group were recorded and compared. The American Orthopedic Foot and Ankle Society (AOFAS) ankle and hindfoot score was used to evaluate ankle function, including pain, quality of daily life, joint range of motion, and joint stability. The AOFAS scores were compared between fixation and non-fixation groups in each group.ResultsThe procedure was successfully completed by all patients in each group, and there was no significant difference in operation time (F=3.677, P=0.159). All patients were followed up 12-36 months with a mean time of 16.8 months. At last follow-up, 6 patients were found to have suboptimal ankle planarity, including 2 patients (5.3%) in the type Ⅰ group and 4 patients (13.3%) in the type Ⅱ group, with no significant difference between groups (χ2=6.566, P=0.161). The ankle joints of all the patients in each group showed mild degeneration; the fractures all healed well and no delayed union or nonunion occurred. There was no significant difference in the fracture healing time and full weight-bearing time between groups (P>0.05). No complications such as incision infection, fracture displacement, or plate screw loosening and fracture occurred during follow-up. At last follow-up, the total scores and pain scores of the AOFAS scores in the type Ⅱ group were significantly lower than those in the type Ⅰand Ⅲ groups (P<0.05), there was no significant difference between groups in the scores for the quality of daily life, joint range of motion, and joint stability between groups (P>0.05). There was no significant difference in any of the scores between the unfixed and fixed groups, except for the pain and quality of daily life scores, which were significantly lower (P<0.05) in the unfixed group of type Ⅱ group than the fixed group.ConclusionHaraguchi type Ⅱ posterior malleolus fractures have a worse prognosis than types Ⅰ and Ⅲ fractures, especially in terms of postoperative pain, which can be significantly improved by fixing the posterior malleolus; the presence or absence of posterior malleolus fixation in types Ⅰ and Ⅲ has less influence on prognosis.
Regurgitation is an abnormal condition happens when left ventricular assist devices (LVADs) operated at a low speed, which causes LVAD to fail to assist natural blood-pumping by heart and thus affects patients’ health. According to the degree of regurgitation, three LVAD’s regurgitation states were identified in this paper: no regurgitation, slight regurgitation and severe regurgitation. Regurgitation index (RI), which is presented based on the theory of dynamic closed cavity, is used to grade the regurgitation of LVAD. Numerical results showed that when patients are in exercising, resting and sleeping state, the critical speed between slight regurgitation and no regurgitation are 6 650 r/min, 7 000 r/min and 7 250 r/min, respectively, with corresponding RI of 0.401, 0.300 and 0.238, respectively. And the critical speed between slight regurgitation and severe regurgitation are 5 500 r/min, 6 000 r/min and 6 450 r/min, with corresponding RI of 0.488, 0.359 and 0.284 respectively. In addition, there is a negative relation correction between RI and rotational speed, so that grading the LVAD’s regurgitation can be achieved by determining the corresponding critical speed. Therefore, the detective parameter RI based on the signal of flow is proved to be able to grade LVAD’s regurgitation states effectively and contribute to the detection of LVAD’s regurgitation, which provides theoretical basis and technology support for developing a LVADs controlling system with high reliability.
