Biocybernetics and Biomedical Engineering最新文献

Significance

The biomechanical properties of the cornea are important for vision and ocular health. Optical coherence elastography (OCE) has the potential to improve our capacity to measure these properties.

Aim

This study tested a static compression OCE method utilising a commercially available optical coherence tomography (OCT) device, to estimate the Young’s modulus of ex-vivo porcine corneal tissue.

Approach: OCT was used to image corneal tissue samples before and during loading by static compression. The compressive force was measured with a piezoresistive force sensor, and tissue deformation was quantified through automated image analysis. Ten ex-vivo porcine corneas were assessed and the corneal thickness was also measured to assess the impact of corneal swelling.

Results

An average (standard deviation) Young’s modulus of 0.271 (+/- 0.091) MPa was determined across the 10 corneas assessed. There was a mean decrease of 1.78 % in corneal thickness at the end of the compression series. These results showed that there was a moderate association between corneal thickness and the Young’s modulus recording (R² = 0.274).

Conclusions

Optical coherence elastography utilising clinical instrumentation, can reliably characterise the mechanical properties of the cornea. These results support the further investigation of the technique for in-vivo measurement of the mechanical properties of the human cornea.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological condition, typically manifesting in childhood. Behavioral studies are used to treat the illness, but there is no conclusive way to diagnose it. To comprehend changes in the brain, electroencephalography (EEG) signals of ADHD patients are frequently examined. In the proposed study, we introduce EEG feature map (EEG-FM)-based image construction to input deep learning architectures for classifying ADHD. To demonstrate the effectiveness of the proposed method, EEG data of 15 ADHD patients and 18 control subjects are analyzed and detection performance is presented. EEG-FM-based images are obtained using both traditional time domain features used in EEG analysis, such as Hjorth parameters (activity, mobility, complexity), skewness, kurtosis, and peak-to-peak, and nonlinear features such as the largest Lyapunov Exponent, correlation dimension, Hurst exponent, Katz fractal dimension, Higuchi fractal dimension, and approximation entropy. EEG-FM-based images are used to train DarkNet19 architecture and deep features are extracted for each image dataset. Fewer deep features are chosen for each image dataset using the Minimum Redundancy Maximum Relevance (mRMR) feature selection method, and the concatenated deep feature set is created by merging the selected features. Finally, various machine learning methods are used to classify the concatenated deep features. Our EEG-FM and DarkNet19-based approach yields classification accuracies for ADHD between 96.6% and 99.9%. Experimental results indicate that the use of EEG-FM-based images as input to DarkNet19 architecture gives significant advantages in the detection of ADHD.

The aim of this study was to introduce and evaluate the baroreflex entropy index (BEI), a novel tool derived from standard lead II electrocardiograph (EKG) for autonomic function (AF) assessment in type 2 diabetes mellitus (T2DM). Researchers with distinct roles (analysis and data preparation) analyzed anonymized EKG data from healthy controls and two patient groups with T2DM (well controlled and poorly controlled). BEI was compared between groups, and correlations with glycemic markers (HbA1c, fasting glucose) were investigated. Logistic regression was used to assess the association between BEI and T2DM risk. BEI showed good repeatability and differentiation between groups. Notably, it required only single-lead EKG. BEI was inversely correlated with glycemic markers, suggesting improved baroreflex regulation with better glycemic control. BEI also outperformed small-scale multiscale entropy in group discrimination. Logistic regression identified BEI as a protective factor for T2DM. BEI represents a promising tool for monitoring AF, assessing glycemic control, and potentially stratifying T2DM risk. Further validation in larger longitudinal studies and an exploration of the applicability of BEI to other diseases are warranted.

“Y-shaped” airway stents have been widely used in the treatment of airway diseases, especially airway stenosis, due to their excellent flexibility. However, the current research on the flexibility of “Y-shaped” airway stents is still blank, limiting the possibility of improving the performance of stents in complex clinical disease. This study aimed to establish multi-dimensional evaluation of the flexibility of a novel segmented “Y-shaped” airway stent and two kinds of conventional stents. We evaluated the flexibility of the segmented stent, wholly knitted stent, and silicone stent by in vitro mechanical testing and finite element analysis methods. That is, the bending force and spring-back force of three kinds of stent were measured in left–right, anterior-posterior and longitudinal directions. The torque of the stents in torsion-recovery test of branches of stent was also executed. Finite element analysis was performed to evaluate the change of diameter. According to the detection, the bending force and spring-back force of the branch of the segmented stent during left–right and anterior-posterior compression, and the torque during torsion and recovery were lower than those of the other two stents. In finite element analysis, the diameter change of the segmented stent was minimal among the three stents. The flexibility of the segmented “Y-shaped” airway stent was better than that of the conventional “Y-shaped” airway stents, indicating that it has better adaptability and resistance to compression when implanted in the body.

Parkinson’s disease (PD) is a neurodegenerative disorder that influence brain’s neurological, behavioral, and physiological functions and includes motor and nonmotor manifestations. Although there have been several PD diagnosis systems with supervised machine learning techniques, there are more efforts that need to enhance the accurate detection of PD in its early stage. The current paper developed a novel approach by integrating Least Squares Support Vector Regression (LS-SVR) and Fuzzy Clustering for Unified Parkinson’s Disease Rating Scale (UPDRS) diagnosis. This paper used feature selection and Principal Component Analysis (PCA) to overcome the multicollinearity issues in data. This paper used a large medical dataset including Motor- and Total-UPDRS to demonstrate how the proposed method can improve prediction performance via extensive evaluations and comparisons with existing methods. Compared to other prediction methods, the experimental results demonstrate that the proposed method provided the best accuracy for Total-UPDRS (Root Mean Squared Error = 0.7348; R² = 0.9169) and Motor-UPDRS (Root Mean Squared Error = 0.8321; R² = 0.8756) predictions.

Convolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to non-optimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly.

Brain cancer, one of the leading causes of mortality worldwide, is caused by brain tumors. Early diagnosis of tumors and predicting their progression can help doctors to save lives. In this article, we have designed an automated approach for locating and classifying tumors from MRI images. The novelties of the research work include the following two stages: Developing an encoder-decoder type 20-Layered deep neural network (DNN) named MultiTumor Analyzer (MTA-20) with 15 down-sampling layers and 4 up-sampling layers, the segmentation is performed in the initial stage. Here, we have adhered a Leaky ReLU activation function instead of ReLU which learn a parameter with negative values that may have valuable information which is essential specifically for image segmentation. Further, a 55-layered DNN using multistage feature fusion is developed in the second stage of the work for the classification of localized tumors. The classification is performed using developed MultiTumor Analyzer (MTA-55) DNN with Softmax classifier. The efficacy of the designed network is validated using highly cited quantitative measures such as accuracy, sensitivity, specificity, dice similarity coefficient (DSC), precision, and F1-measure. It is observed that the proposed MTA-20 DNN attains the average accuracy, sensitivity, specificity, DSC, and precision of 99.2 %, 94.6 %, 99.3 %, 88 %, and 82.5 % respectively against seven state-of-the-art techniques. Also, it is found that, the proposed MTA-55 DNN provides the overall accuracy, recall, specificity, F1-measure, precision, and DSC of 99.8 %, 99.633 %, 99.844 %, 99.659 %, 99.689 %, and 99.656 % respectively as compared to thirteen state-of-the-art techniques. These results corroborate the superiority of the proposed technique.

In addressing the challenges of medical image segmentation, particularly the elusiveness of global context and limitations in leveraging both global and local context simultaneously, we present SegmentNet as a solution. Our approach involves a step-by-step implementation within the reconstructed UNet architecture, tailored to enhance segmentation performance across diverse medical imaging modalities. The first step involves the integration of multi-focus Distance-Aware Mechanisms (DaMs) within skip connections and between successive layers of the encoder in SegmentNet. This strategic placement focuses on extracting unrelated features, ensuring comprehensive consideration of global context. Following this, Local Feature Extractor Blocks (LFEBs) are introduced at the base of the network. Equipped with depthwise separable operations, standard convolutions, smoothed ReLU, and normalization transform, LFEBs target the capture of specific local image features ensuring that features overlooked by DaMs are appropriately considered. These extracted features are then passed on to the decoder portion of SegmentNet, facilitating enhanced prediction of masks thus, optimizing segmentation performance. Evaluated across diverse datasets, including Breast Ultrasound Images (BUSI), Chest X-ray images (CXRI), and Diabetic Retinal Fundus Images (DRFI), SegmentNet excels. The segmentation evaluation results in terms of accuracy, Jaccard, and specificity are respectively recorded for BUSI, CXRI, and DRFI to be (93.88 %, 98.96 %, and 99.17 %), (99.28 %, 99.58 %, and 99.83 %), and (95.77 %, 95.95 %, and 99.94 %). Thus, showing that the incorporation of DaMs and LFEBs in SegmentNet emerges as a robust solution demonstrating precise 2D medical image segmentation across various modalities. This advancement holds significant potential for diverse clinical applications, promising improved patient care.

Squamous cell carcinoma is the most common type of cancer that occurs in many organs of the human body. To detect carcinoma, pathologists observe tissue samples at multiple magnifications, which is time-consuming and prone to inter- or intra-observer variability. The key challenge for automation of squamous cell carcinoma diagnosis is to extract the features at low (100x) magnification and explain the decision-making process to healthcare professionals. The existing literature used either machine learning or deep learning models to detect squamous cell carcinoma of specific organs. In this work, we report on the implementation of an explainable diagnostic aid system for squamous cell carcinoma of any organ and present a comparative analysis with state-of-the-art models. A classifier with an ensemble feature selection technique is developed to provide an automatic diagnostic aid for distinguishing between squamous cell carcinoma positive and negative cases based on histopathological images. Moreover, explainable AI techniques such as ELI5, LIME and SHAP are introduced to machine learning model which provides feature interpretability of prediction made by the classifier. The results show that the machine learning model achieved an accuracy of 93.43% and 96.66% on public and multi-centric private datasets, respectively. The proposed CatBoost classifier achieved remarkable performance in diagnosing multi-organ squamous cell carcinoma from low magnification histopathological images, even when various illumination variations were introduced.

Sonothrombolysis induces clot breakdown using ultrasound waves to excite microbubbles. Despite the great potential, selecting optimal ultrasound (frequency and pressure) and microbubble (radius) parameters remains a challenge. To address this, a computational model was developed to investigate the bubble behaviour during sonothrombolysis. The blood and clot were assumed to be non-Newtonian and porous, respectively. The effects of ultrasound and microbubble parameters on flow-induced shear stress on the clot surface during stable and inertial cavitation were investigated. It was found that microbubble translation towards the clot and the shear stress on the clot surface during stable cavitation were significant when the bubble was about to undergo inertial cavitation. While insonation of large microbubble (radius of $1.65\mu m$) at low frequency (0.50 MHz) produced the highest shear stress during stable cavitation, selection of these parameters is not as intuitive for inertial cavitation due to the strong competing effect between jet velocity and translational distance. An increase in jet velocity is always accompanied by a decrease in the translational distance and vice versa. Therefore, a right balance between the jet velocity and the translational distance is critical to maximise the shear stress on the clot surface. A jet velocity of 303 m/s and a distance travelled of $5.12\mu m$ at an initial bubble-clot separation of $10\mu m$ produced the greatest clot surface shear stress. This is achievable by insonating a $0.55\mu m$ microbubble using 0.50 MHz and 600 kPa ultrasound.