Clinical and Radiological Features of Unilateral Posterior Reversible Encephalopathy Syndrome (PRES): A Case Report.

Posterior reversible encephalopathy syndrome (PRES) is a clinicoradiological entity characterized by reversible vasogenic edema predominantly affecting the posterior regions of the cerebral hemispheres. However, unilateral presentation of PRES is an exceptionally rare manifestation. We describe the case of a 34-year-old woman who presented with left-sided hemiparesis, hemisensory loss, headache, and focal motor seizures. Brain CT revealed right anterior and posterior hypodensities in the right frontal and parietal subcortical locations, brain MRI showed vasogenic edema in the subcortical right cerebral hemisphere, and cerebral angiogram revealed diffuse narrowing of the left internal carotid artery just distal to the carotid bifurcation with no flow of contrast beyond the ophthalmic segment. The patient's symptoms resolved during her admission, MRI findings improved on repeated imaging, and she was ultimately diagnosed with unilateral PRES. Unilateral PRES is a complex and challenging diagnosis, and this case sheds light on the atypical radiological features of unilateral PRES possibly intricately linked with contralateral steno-occlusive disease of the carotid artery. It is essential to maintain the atypical variants of PRES as part of the differential diagnosis when encountering acute neurological symptoms and vasogenic edema on imaging in the context of contralateral steno-occlusive disease of the carotid artery.

A Cautionary Note on Pembrolizumab use in Patients with Ascending Aortic Aneurysms.

Case description: We describe a case of a patient treated with pembrolizumab (an immune checkpoint inhibitor) for metastatic scalp melanoma. He had a previous history of colorectal cancer, prostatic cancer and chronic polymyalgia rheumatica. The patient was known to have a stable ascending aortic aneurysm of 4.5 cm. However, he developed a rapid expansion of the ascending aortic aneurysm with the size crossing the threshold for surgery. The patient was referred to the cardiothoracic surgery service for intervention and he subsequently underwent surgery. The patient was electively admitted one week later for resection of aortic aneurysm, aortoplasty and external graft fixation. Pathologically, gross evidence of dissection was not identified; however, the histological analysis of the media showed laminar medial necrosis, multifocal in nature, with occasional clusters of histiocytic cells appreciated at their edge reminiscent of that seen in an inflammatory aortitis (granulomatous/giant cell type). Discussion: Immune checkpoint inhibitor-induced aortitis is becoming increasingly evident, and its presentation can vary. It has been discovered incidentally on surveillance imaging with the use of nivolumab. In other cases, patients have been symptomatic to severely symptomatic. Atezolizumab with carboplatin and etoposide has been reported to cause abdominal aortitis which was responsive to corticosteroids and subsequent discontinuation of atezolizumab. Pembrolizumab has been linked to a case of transverse aortic arch aortitis. In our case, the inflammatory aortitis due to pembrolizumab was the cause of the rapid expansion of the ascending aortic aneurysm. Conclusion: Patients with known aortic aneurysms should undergo careful surveillance when commencing immune-checkpoint inhibitor therapy. LEARNING POINTS: Immune checkpoint inhibitors are being increasingly used in the treatment of metastatic malignancy. However, they are a relatively new group of medications, and the side effect profile of each is yet to be fully recognised. Aortitis has occurred with several different immune checkpoint inhibitors.Patients with known aortic aneurysms should undergo careful surveillance when commencing immune checkpoint inhibitors.All interventional therapeutic options should be considered early in these patients on the development of aneurysmal expansion.

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible "Paediatric Intensive Care database" to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

ECG waveform generation from radar signals: A deep learning perspective.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.

FPGA Implementation of Complex-Valued Neural Network for Polar-Represented Image Classification.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a Field-Programmable Gate Array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6 ms and a power consumption of 4.66 mW for the classification of the MNIST test dataset, which consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency-surpassing existing models by more than a factor of 100. In conclusion, this paper demonstrates the substantial advantages of the FPGA implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical.

Dynamic Imaging of Blood Coagulation Within the Hematoma of Patients With Acute Hemorrhagic Stroke.

BACKGROUND: The dynamics of blood clot (combination of Hb [hemoglobin], fibrin, and a higher concentration of aggregated red blood cells) formation within the hematoma of an intracerebral hemorrhage is not well understood. A quantitative neuroimaging method of localized coagulated blood volume/distribution within the hematoma might improve clinical decision-making. METHODS: The deoxyhemoglobin of aggregated red blood cells within extravasated blood exhibits a higher magnetic susceptibility due to unpaired heme iron electrons. We propose that coagulated blood, with higher aggregated red blood cell content, will exhibit (1) a higher positive susceptibility than noncoagulated blood and (2) increase in fibrin polymerization-restricted localized diffusion, which can be measured noninvasively using quantitative susceptibility mapping and diffusion tensor imaging. In this serial magnetic resonance imaging study, we enrolled 24 patients with acute intracerebral hemorrhage between October 2021 to May 2022 at a stroke center. Patients were 30 to 70 years of age and had a hematoma volume >15 cm3 and National Institutes of Health Stroke Scale score >1. The patients underwent imaging 3×: within 12 to 24 (T1), 36 to 48 (T2), and 60 to 72 (T3) hours of last seen well on a 3T magnetic resonance imaging system. Three-dimensional anatomic, multigradient echo and 2-dimensional diffusion tensor images were obtained. Hematoma and edema volumes were calculated, and the distribution of coagulation was measured by dynamic changes in the susceptibilities and fractional anisotropy within the hematoma. RESULTS: Using a coagulated blood phantom, we demonstrated a linear relationship between the percentage coagulation and susceptibility (R2=0.91) with a positive red blood cell stain of the clot. The quantitative susceptibility maps showed a significant increase in hematoma susceptibility (T1, 0.29±0.04 parts per millions; T2, 0.36±0.04 parts per millions; T3, 0.45±0.04 parts per millions; P<0.0001). A concomitant increase in fractional anisotropy was also observed with time (T1, 0.40±0.02; T2, 0.45±0.02; T3, 0.47±0.02; P<0.05). CONCLUSIONS: This quantitative neuroimaging study of coagulation within the hematoma has the potential to improve patient management, such as safe resumption of anticoagulants, the need for reversal agents, the administration of alteplase to resolve the clot, and the need for surgery.

Quantification of vesicoureteral reflux using machine learning.

INTRODUCTION: The radiographic grading of voiding cystourethrogram (VCUG) images is often used to determine the clinical course and appropriate treatment in patients with vesicoureteral reflux (VUR). However, image-based evaluation of VUR remains highly subjective, so we developed a supervised machine learning model to automatically and objectively grade VCUG data. STUDY DESIGN: A total of 113 VCUG images were gathered from public sources to compile the dataset for this study. For each image, VUR severity was graded by four pediatric radiologists and three pediatric urologists (low severity scored 1-3; high severity 4-5). Ground truth for each image was assigned based on the grade diagnosed by a majority of the expert assessors. Nine features were extracted from each VCUG image, then six machine learning models were trained, validated, and tested using 'leave-one-out' cross-validation. All features were compared and contrasted, with the highest-ranked then being used to train the final models. RESULTS: F1-score is a metric that is often used to indicate performance accuracy of machine learning models. When using the highest-ranked VCUG image features, F1-scores for the support vector machine (SVM) and multi-layer perceptron (MLP) classifiers were 90.27 % and 91.14 %, respectively, indicating a high level of accuracy. When using all features combined, F1 scores were 89.37 % for SVM and 90.27 % for MLP. DISCUSSION: These findings indicate that a distorted pattern of renal calyces is an accurate predictor of high-grade VUR. Machine learning protocols can be enhanced in future to improve objective grading of VUR.

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management.

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite.

This research investigates the implementation of complex-exponential-based neurons in FPGA, which can pave the way for implementing bio-inspired spiking neural networks to compensate for the existing computational constraints in conventional artificial neural networks. The increasing use of extensive neural networks and the complexity of models in handling big data lead to higher power consumption and delays. Hence, finding solutions to reduce computational complexity is crucial for addressing power consumption challenges. The complex exponential form effectively encodes oscillating features like frequency, amplitude, and phase shift, streamlining the demanding calculations typical of conventional artificial neurons through levering the simple phase addition of complex exponential functions. The article implements such a two-neuron and a multi-neuron neural model using the Xilinx System Generator and Vivado Design Suite, employing 8-bit, 16-bit, and 32-bit fixed-point data format representations. The study evaluates the accuracy of the proposed neuron model across different FPGA implementations while also providing a detailed analysis of operating frequency, power consumption, and resource usage for the hardware implementations. BRAM-based Vivado designs outperformed Simulink regarding speed, power, and resource efficiency. Specifically, the Vivado BRAM-based approach supported up to 128 neurons, showcasing optimal LUT and FF resource utilization. Such outcomes accommodate choosing the optimal design procedure for implementing spiking neural networks on FPGAs.

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

Advancement in total hip implant: a comprehensive review of mechanics and performance parameters across diverse novelties.

The UK's National Joint Registry (NJR) and the American Joint Replacement Registry (AJRR) of 2022 revealed that total hip replacement (THR) is the most common orthopaedic joint procedure. The NJR also noted that 10-20% of hip implants require revision within 1 to 10 years. Most of these revisions are a result of aseptic loosening, dislocation, implant wear, implant fracture, and joint incompatibility, which are all caused by implant geometry disparity. The primary purpose of this review article is to analyze and evaluate the mechanics and performance factors of advancement in hip implants with novel geometries. The existing hip implants can be categorized based on two parts: the hip stem and the joint of the implant. Insufficient stress distribution from implants to the femur can cause stress shielding, bone loss, excessive micromotion, and ultimately, implant aseptic loosening due to inflammation. Researchers are designing hip implants with a porous lattice and functionally graded material (FGM) stems, femur resurfacing, short-stem, and collared stems, all aimed at achieving uniform stress distribution and promoting adequate bone remodeling. Designing hip implants with a porous lattice FGM structure requires maintaining stiffness, strength, isotropy, and bone development potential. Mechanical stability is still an issue with hip implants, femur resurfacing, collared stems, and short stems. Hip implants are being developed with a variety of joint geometries to decrease wear, improve an angular range of motion, and strengthen mechanical stability at the joint interface. Dual mobility and reverse femoral head-liner hip implants reduce the hip joint's dislocation limits. In addition, researchers reveal that femoral headliner joints with unidirectional motion have a lower wear rate than traditional ball-and-socket joints. Based on research findings and gaps, a hypothesis is formulated by the authors proposing a hip implant with a collared stem and porous lattice FGM structure to address stress shielding and micromotion issues. A hypothesis is also formulated by the authors suggesting that the utilization of a spiral or gear-shaped thread with a matched contact point at the tapered joint of a hip implant could be a viable option for reducing wear and enhancing stability. The literature analysis underscores substantial research opportunities in developing a hip implant joint that addresses both dislocation and increased wear rates. Finally, this review explores potential solutions to existing obstacles in developing a better hip implant system.

Explainable deep learning model for automatic mulberry leaf disease classification.

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world's raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models' findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves.

Synthesis, Properties, Applications, and Future Prospective of Cellulose Nanocrystals.

The exploration of nanocellulose has been aided by rapid nanotechnology and material science breakthroughs, resulting in their emergence as desired biomaterials. Nanocellulose has been thoroughly studied in various disciplines, including renewable energy, electronics, environment, food production, biomedicine, healthcare, and so on. Cellulose nanocrystal (CNC) is a part of the organic crystallization of macromolecular compounds found in bacteria's capsular polysaccharides and plant fibers. Owing to numerous reactive chemical groups on its surface, physical adsorption, surface grating, and chemical vapor deposition can all be used to increase its performance, which is the key reason for its wide range of applications. Cellulose nanocrystals (CNCs) have much potential as suitable matrices and advanced materials, and they have been utilized so far, both in terms of modifying and inventing uses for them. This work reviews CNC's synthesis, properties and various industrial applications. This review has also discussed the widespread applications of CNC as sensor, acoustic insulator, and fire retardant material.

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.

BACKGROUND: Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. METHODS: PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. RESULTS: This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation. CONCLUSIONS: This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model.

Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face-hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron "SelfMLP" is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face-hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.

A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images.

Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions.

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

Oxygen releasing patches based on carbohydrate polymer and protein hydrogels for diabetic wound healing: A review.

Diabetic wounds are among the major healthcare challenges, consuming billions of dollars of resources and resulting in high numbers of morbidity and mortality every year. Lack of sufficient oxygen supply is one of the most dominant causes of impaired healing in diabetic wounds. Numerous clinical and experimental studies have demonstrated positive outcomes as a result of delivering oxygen at the diabetic wound site, including enhanced angiogenesis, antibacterial and cell proliferation activities. However, prolonged and sustained delivery of oxygen to improve the wound healing process has remained a major challenge due to rapid release of oxygen from oxygen sources and limited penetration of oxygen into deep skin tissues. Hydrogels made from sugar-based polymers such as chitosan and hyaluronic acid, and proteins such as gelatin, collagen and hemoglobin have been widely used to deliver oxygen in a sustained delivery mode. This review presents an overview of the recent advances in oxygen releasing hydrogel based patches as a therapeutic modality to enhance diabetic wound healing. Various types of oxygen releasing wound healing patch have been discussed along with their fabrication method, release profile, cytocompatibility and in vivo results. We also briefly discuss the challenges and prospects related to the application of oxygen releasing biomaterials as wound healing therapeutics.