Arthroscopic rotator cuff repair is widely used clinically, but the phenomenon of re-tear after repair is still common. Due to the special structure of the tendon-bone junction, the promotion of tissue regeneration from the perspective of biological enhancement has attracted attention. Platelet-rich plasma (PRP) is a supraphysiological concentration of autologous platelets, which can promote the healing of rotator cuff injury after repair. However, due to the lack of clinical use standards, not all PRPs are the same, there are clear differences between liquid PRP and solid platelet-rich fibrin, and many studies have not differentiated their properties. This article reviews the research progress of different types of PRP in the repair of rotator cuff injury, aiming to provide some reference for clinical treatment selection.
Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, QAB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.
Speech enhancement methods based on microphone array adopt many microphones to record speech signal simultaneously. As spatial information is increased, these methods can increase speech recognition for cochlear implant in noisy environment. Due to the size limitation, the number of microphones used in the cochlear implant cannot be too large, which limits the design of microphone array beamforming. To balance the size limitation of cochlear implant and the spatial orientation information of the signal acquisition, we propose a speech enhancement and beamforming algorithm based on dual thin uni-directional / omni-directional microphone pairs (TP) in this paper. Each TP microphone contains two sound tubes for signal acquisition, which increase the overall spatial orientation information. In this paper, we discuss the beamforming characteristics with different gain vectors and the influence of the inter-microphone distance on beamforming, which provides valuable theoretical analysis and engineering parameters for the application of dual microphone speech enhancement technology in cochlear implants.
Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group (P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.
ObjectiveTo investigate the diagnostic performance of parameters of arterial enhancement fraction (AEF) based on enhanced CT with histogram analysis in the severity of liver cirrhosis.MethodsThe patients with liver cirrhosis clinically confirmed and met the inclusion criteria were included from January 2016 to December 2018 in the First Affiliated Hospital of Chengdu Medical College, then them were divided into grade A, B, and C according to the Child-Pugh score. Meanwhile, the patients without liver disease were selected as the control group. All patients underwent the upper abdomen enhanced CT scan with three-phase and the biochemical examination of liver function. The parameters of AEF histogram were obtained by using the CT Kinetics software, and the aspartic aminotransferase and platelet ratio index (APRI) was calculated. The differences of parameters of AEF histogram and APRI among these patients with liver cirrhosis and without liver disease were analyzed. The diagnostic performance was evaluated by using the area under curve (AUC) of receivers operating characteristic curve.ResultsEighty-five patients with liver cirrhosis were included in this study, including 25, 41, and 19 patients with grade A, B, and C of Child-Pugh score, respectively, and there were 20 patients in the control group. The consistencies in measuring the parameters of AEF histogram twice for the same observer and between the two observers were good (intraclass correlation coefficient was 0.938 and 0.907, respectively). The mean, median, and kurtosis of AEF histogram and the APRI among the grade A, B, C of Child-Pugh score, and control group had significant differences (all P<0.001) and these indexes were positively correlated with the severity of liver cirrhosis (rs=0.811, P<0.001; rs=0.827, P<0.001; rs=0.731, P<0.001; rs=0.711, P<0.001). The AUC of the mean, median, kurtosis, and APRI in diagnosing grade A of liver cirrhosis was 0.829, 0.841, 0.747, and 0.718, respectively; which in diagnosing grade B of liver cirrhosis was 0.847, 0.734, 0.704, and 0.736, respectively; in diagnosing grade C of liver cirrhosis was 0.646, 0.825, 0.782, and 0.853, respectively.ConclusionThe mean and median of AEF histogram parameters based on enhanced CT with three-phase and serological APRI are useful in diagnosis of grage A, B, and C of liver cirrhosis, respectively.
The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network’s focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
Objective To summarize ultrasonography, CT and (or) MRI imaging features of cystic liver lesions so as to improve its diagnostic accuracy. Methods The literatures relevant imaging studies of different types of cystic liver lesions at home and abroad were searched. Then with the etiology as clue, the imaging fetures of ultrasonography, CT and (or) MRI plain scan and enhancement scan were summarized. Results The cystic liver lesions had many types, their imaging findings were different and existed overlaps. The diagnosis and differential diagnosis of atypical cases were difficult. ① For the simple hepatic cyst, it was a round cystic mass with water-like echo, density and signal. The boundary was clear, and there was no separation in the cyst, without contrast enhancement. The sensitivity and specificity of diagnosing were higher by ultrasonography and MRI as compared with CT. ② For the bile duct hamartoma and Caroli diease, they were manifested as multiple cysts, widely distributed in the whole liver, without enhancement for the most lesions. The multiple cystic lesions without communicating with the bile duct was the key sign of differential diagnosis for these two dieases. ③ Enhancing mural nodules were more common in cystadenocarcinoma than cystadenoma. The accurate diagnosis of biliary cystadenoma depended on combination of ultrasonography, CT, and MRI findings. ④ For the cystic liver metastatic tumor, it was multiple cystic neoplasms in the liver parenchyma or around the liver. CT was the main method for the diagnosis, and which showed that the density was lower than that of the liver parenchyma, peripheral ring-enhanced lesion as enhanced scan. It was easy to distinguish with simple hepatic cyst by MRI. ⑤ For the cystic hepatocellular carcinoma, it presented as a multilocular cystic solid tumor. The presence of tumor thrombus in portal vein could help to the diagnosis. ⑥ For the undifferentiated embryonal sarcoma, CT plain scan showed the cystic low density mass with clear boundary, the edge with calcification, enhanced scan showed that the soft tissue composition presented continuous strengthening sign. There was no specific signal in MRI plain scan, and the periphery of the tumor was slowly strengthening. ⑦ For the liver abscess, it was easy to diagnose because it had different characteristic features in different pathological phase, but it was misdiagnosis of intrahepatic cholangiocarcinoma when its symptoms were atypical. ⑧ The ultrasonography and the CT were the optimal methods for the hepatic cystic echinococcosis and the hepatic alveolar echinococcosis respectively. The significances of imaging were to determine the activity of hydatid cyst and to identify anatomy structure among alveolar echinococcosis, bile duct and blood vessel, and judge invasion or not, MRCP was important for diagnosis. Conclusions Abdominal ultrasonography could be used as the first choice for diagnosis of cystic liver lesions, CT and MRI could be used as effective supplementary methods for it. A combination of various imaging techniques is key to diagnosis. Moreover, number and morphology of lesion, and solid component or not are important imaging features of diagnosis and differential diagnosis of cystic liver lesion.