ObjectiveTo introduce and interpret ABCD classification system for subaxial cervical spine injury. MethodsThe literature related to subaxial cervical spine injury classification system was extensively reviewed, analyzed, and summarized so as to introduce the ABCD classification system. ResultsThe ABCD classification system for subaxial cervical spine injury consists of 3 parts. The first part of the proposed classification is an anatomical descri ption of the injury; it del ivers the information whether injury is bony, ligamentous, or a combined one. The second part is the classification of nerve function, spinal stenosis, and spinal instabil ity. The last part is optional and denotes radiological examination which is used to define injury type. Several letters have been used for simplicity to del iver the largest amount of information. And a treatment algorithm based on the proposed classification is suggested. ConclusionThe ABCD classification system is proposed for simplification. However further evaluation of this classification is needed.
Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that damages patients’ memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
One hundred and thirty patients with uveitis in north-western zone of our country were analyzed based on anatomical classification and their causes. It was found that anterior uveitis was the commonest type in uveitis,accounting for 86.15% of total patients. Intermediate uveitis, pan-uveitis and posterior uveitis accounted repectively for 6.92%, 3.85%and3.08% of the total patients. Rheumatic arthritis was the most frequently accompanied systemic disease in patients with uveitis,showing a possibly causative link between them in their pathogenesis. （Chin J Ocul Fundus Dis,1994,10:156-158）
Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.
ObjectiveTo evaluate the differences of visual evoked potentials (amplitudes and latency) between cerebral palsy (CP) children and normal children. MethodsThis study involved fourteen children aged from 4 to 7 years with CP (monoplegia) between 2009 and 2013. Another 14 normal children aged from 5 to 9 years treated in the Department of Ophthalmology in West China Hospital during the same period were regarded as the control group. Both eyes of all the participants were examined by multifocal visual evoked potential (mfVEP). The mfVEP examination results were recorded, and amplitude and latency were analyzed. First, we analyzed the differences of amplitudes and latency time between monoplegia children and children in the control group. Second, gross motor function classification system (GMFCS) was used to classify the fourteen monoplegia children among whom there were five GMFCS Ⅰ patients and nine GMFCS Ⅱ patients. The differences of mfVEP were analyzed between the two GMFCS groups. ResultsThe amplitude and latency of mfVEP in children with CP showed gradual changes similar to those in the normal children. The amplitudes were decreasing and the latencies were delaying from the first eccentricity to the sixth eccentricity. The amplitudes in children with CP were lower than those in the control group in the first to the third eccentricities for both eyes (P＜0.05), and latency of left eye was delayed in the first eccentricity in children with CP (P=0.045). No difference was found between the two GMFCS groups (P>0.05) except the amplitude of the first eccentricity (P=0.043). ConclusionsThe results of mfVEP show significant differences of amplitude and latency between CP and normal children, suggesting the existence of visual pathway impairments in cerebral palsy children. The results of mfVEP can provide an objective basis of visual impairments for cerebral palsy children.
Purpose To analyse the maculopathy in 597 eyes of 317 cases with diabetic retinopathy,and to explore the classification and visual prognosis. Methods Using fluorescein angiography to examine the extend of capillary leakage and foveal avascular zone as well as the extent of the capillary closure in macular area. Results ①Diabetic maculopathy was divided into 5 types,among 597 eyes,no leakage type 154 eyes (25.8%),focal edema type 188 eyes(31.5%),diffuse edema type (including cystoid edema)231 eyes(40.0%),ischemic type 12 eyes(2.0%) and proli ferative type was 4 eyes(0.7%).② There is close relationship between the classification and visual prognosis.such as when visual acuity was ge;0.5,no leakage type was 99.4%, focal edema type was 83.0%,diffuse edema type was 28.4%,ischemic type was 8.4%,and proliferative type was 0.5%.the visual acuity of cystoid edema was worse than diffuse edema only 20.3%.③The stage and visual prognosis:The higher the stage the worse the visual prognosis.if visual acuityge;0.5, 1 stage in 96.2% eyes,2 stage in 84.8%,3 stage in 53.2%,4 stage in 37.2%,5 stage in 12.5%. Conclusion Diabetic maculopathy is the main cause of visual impairment in diabetic retinopathy. Different type has different visual prognosis.macular edema and cystoid edema are the main factors to decrease visual acuity and could be treated by focal and grid laser photocoagulation to prevent visual loss. (Chin J Ocul Fundus Dis,2000,16:144-146)
Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
Objective To introduce a modified Sakakibara classification system for a ruptured sinus of Valsalva aneurysm （RSVA），and suggest different surgical approaches for corresponding types of RSVA. Methods Clinical data of 159 patients undergoing surgical repair for RSVA in Fu Wai Hospital between February 2006 and January 2012 were retrospectively analyzed. There were 105 male and 54 female patients with their age of 2-71 （33.4±10.7） years. All these patients were divided into 5 types as a modified Sakakibara classification system. Type I: rupture into the right ventricle just beneath the pulmonary valve （n=66），including 84.8% patients with ventricular septal defect （VSD） and 53.8% patients with aortic valve insufficiency （AI）. TypeⅡ：rupture into or just beneath the crista supraventricularis of the right ventricle （n=17），including 88.2% patients with VSD and 23.5% patients with AI. Type Ⅲ：rupture into the right atrium （typeⅢ a，n=21） or the right ventricle （typeⅢv，n=6） near or at the tricuspid annulus，including 18.5% patients with VSD and 25.9% patients with AI. TypeⅣ：rupture into the right atrium （n=46），including 23.9% patients with AI but no patient with VSD. TypeⅤ：other rare conditions，such as rupture into the left atrium，left ventricle or pulmonary artery （n=3），including 100% patients with AI and 33.3% patients with VSD. Most RSVA originated in the right coronary sinus （n=122），and others originated in the noncoronary sinus （n=35） or left coronary sinus （n=2）. Results All the type V patients （100%） and 50% patients with typeⅢv received RSVA repair through aortotomy. In most patients of typeⅠ，II andⅣ，repair was achieved through the cardiac chamber of the fistula exit （71.2%，64.7% and 69.6% respectively）. Both routes of repair were used in 76.2% patients with typeⅢ a. The cardiopulmonary bypass time （92.4±37.8 minutes） and aortic cross-clamp time （61.2±30.7 minutes） was the shortest to repair typeⅣRSVA. There was no in-hospital death in this group. Two patients （type I andⅡrespectively） underwent reoperation during the early postoperative period because of restenosis of the right ventricular outflow tract. Most patients received reinforcement patch for RSVA repair （n=149），and only 10 patients received simple suture repair （including 5 patients with typeⅣ，4 patients with typeⅢ a and 1 patient with typeⅡ）. Aortic valve replacement was performed for 33 patients （66.7% of those with typeⅠ）. A total of 147 patients （92.5%） were followed up after discharge. Two patients （type I andⅢ a respectively） developed atrial fibrillation and received radiofrequency ablation treatment，1 patient （typeⅣ） underwent reoperation for residual shunt，and there was no late death during follow-up. Conclusion Modified Sakakibara classification system for RVSA provides a guidance to choose an appropriate surgical approach，and satisfactory clinical outcomes can be achieved for all types of RSVA.
Objective To discuss the concept of ulnar tunnel at thewrist, the types, causes, traits of compression, diagnosis, and clinical significance of ulnar tunnel syndrome(UTS). Methods Thirty-nine cases diagnosed as having UTS from 1986 were retrospectively reviewed combined with previous relevant literature. Results Ulnar tunnel included Guyon’s canal, pisohamate tunnel and hypothenar segment. There were 8 types andmany causes of UTS. Some patients had compression in more than one zones and might be associated with carpal tunnel syndrome or cubital tunnel syndrome. UTS could be diagnosed through clinical manifestations and electrophysiological examination. Conclusion Defining the concept of ulnar tunnel and the knowledge of the complexity and rarity of UTS can effectively guide diagnosis and treatment.
Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.