Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images.

Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks. The proposed PE-YNet model is tested with two public datasets, achieving a maximum mean detection and segmentation accuracy of 99.89% and 99.83%, respectively, on the CAD-PE challenge dataset. Similarly, it also achieves a detection accuracy of 99.75% and a segmentation accuracy of 99.81% on the FUMPE dataset. Additionally, sensitivity analysis also shows a high sensitivity of 0.9885 for the localization error ɛ = 0 for the CAD-PE dataset, demonstrating the model's robustness against false predictions compared to state-of-the-art models. Further, this model also exhibits lower inference time, size, and memory usage compared to representative models. An automated PE-YNet tool can assist physicians with PE diagnosis, treatment, and prognosis monitoring in the clinical management of CoVID-19.

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement.

Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.

Time-dependent biomechanical evaluation for corrective planning of scoliosis using finite element analysis - A comprehensive approach.

Scoliosis is a medical condition marked by an abnormal lateral curvature of the spine, typically forming a sideways "S" or "C" shape. Mechanically, it manifests as a three-dimensional deformation of the spine, potentially leading to diverse clinical issues such as pain, diminished lung capacity, and postural abnormalities. This research specifically concentrates on the Adolescent Idiopathic Scoliosis (AIS) population, as existing literature indicates a tendency for this type of scoliosis to deteriorate over time. The principal aim of this investigation is to pinpoint the biomechanical factors contributing to the progression of scoliosis by employing Finite Element Analysis (FEA) on computed tomography (CT) data collected from adolescent patients. By accurately modeling the spinal curvature and related deformities, the stresses and strains experienced by vertebral and intervertebral structures under diverse loading conditions can be simulated and quantified. The transient simulation incorporated damping and inertial terms, along with the static stiffness matrix, to enhance comprehension of the response. The findings of this study indicate a significant reduction in the Cobb angle, halving from its initial value, decreasing from 35° to 17°. In degenerative scoliosis, failure was predicted at 109 cycles, with the Polypropylene brace deforming by 10.34 mm, while the Nitinol brace exhibited significantly less deformation at 7.734 mm. This analysis contributes to a better understanding of the biomechanical mechanisms involved in scoliosis development and can assist in the formulation of more effective treatment strategies. The FEA simulation emerges as a valuable supplementary tool for exploring various hypothetical scenarios by applying diverse loads at different locations to enhance comprehension of the effectiveness of proposed interventions.

Computation of Vascular Parameters: Implementing Methodology and Performance Analysis.

This paper presents the feasibility of automated and accurate in vivo measurements of vascular parameters using an ultrasound sensor. The continuous and non-invasive monitoring of certain parameters, such as pulse wave velocity (PWV), blood pressure (BP), arterial compliance (AC), and stiffness index (SI), is crucial for assessing cardiovascular disorders during surgeries and follow-up procedures. Traditional methods, including cuff-based or invasive catheter techniques, serve as the gold standard for measuring BP, which is then manually used to calculate AC and SI through imaging algorithms. In this context, the Continuous and Non-Invasive Vascular Stiffness and Arterial Compliance Screener (CaNVAS) is developed to provide continuous and non-invasive measurements of these parameters using an ultrasound sensor. By driving 5 MHz (ranging from 2.2 to 10 MHz) acoustic waves through the arterial walls, capturing the reflected echoes, and employing pre-processing techniques, the frequency shift is utilized to calculate PWV. It is observed that PWV measured by CaNVAS correlates exponentially with BP values obtained from the sphygmomanometer (BPMR-120), enabling the computation of instantaneous BP values. The proposed device is validated through measurements conducted on 250 subjects under pre- and post-exercise conditions, demonstrating an accuracy of 95% and an average coefficient of variation of 12.5%. This validates the reliability and precision of CaNVAS in assessing vascular parameters.

Diabetic Foot Assessment and Care: Barriers and Facilitators in a Cross-Sectional Study in Bangalore, India.

(1) Background: This cross-sectional study aims to highlight the assessment and foot care practices in an advanced clinical setting, the clinical characteristics of the patients, and to understand the barriers and facilitators for effective foot care from the perspectives of healthcare practices, resources, and patients' socioeconomic and cultural practices, and other aspects in terms of new technologies for effective foot care such as infrared thermography. (2) Methods: Clinical test data from 158 diabetic patients and a questionnaire to assess the foot care education retention rate were collected at the Karnataka Institute of Endocrinology and Research (KIER) facility. (3) Results: Diabetic foot ulcers (DFUs) were found in 6% of the examined individuals. Male patients were more likely to have diabetes complications, with an odds ratio (OR) of 1.18 (CI = 0.49-2.84). Other diabetes problems raised the likelihood of DFUs by OR 5 (CI = 1.40-17.77). The constraints include socioeconomic position, employment conditions, religious customs, time and cost, and medication non-adherence. The attitude of podiatrists and nurses, diabetic foot education, and awareness protocols and amenities at the facility were all facilitators. (4) Conclusions: Most diabetic foot complications might be avoided with foot care education, regular foot assessments as the standard of treatment, and self-care as a preventive/therapeutic strategy.

The mediating role of attention deficit in relationship between insomnia and social cognition tasks among nurses in Saudi Arabia: A cross-sectional study.

Purpose: Insomnia-related affective functional disorder may negatively affect social cognition such as empathy, altruism, and attitude toward providing care. No previous studies have ever investigated the mediating role of attention deficit in the relationship between insomnia and social cognition. Methods: A cross-sectional survey was carried out among 664 nurses (Mage = 33.03 years; SD ± 6.93 years) from December 2020 to September 2021. They completed the Scale of Attitude towards the Patient (SAtP), the Athens Insomnia Scale (AIS), a single-item numeric rating scale assessing the increasing severity of attention complaints, and questions relating to socio-demographic information. The analysis was carried out by examining the mediating role of attention deficit in the relationship between insomnia and social cognition. Results: The prevalence of insomnia symptoms was high (52% insomnia using the AIS). Insomnia was significantly correlated with attention problems (b = 0.18, standard error (SE) = 0.02, p < 0.001). Attention problems were significantly negatively correlated with nurses' attitudes towards patients (b = -0.56, SE = 0.08, p < 0.001), respect for autonomy (b = -0.18, SE = 0.03, p < 0.001), holism (b = -0.14, SE = 0.03, p < 0.001), empathy (b = -0.15, SE = 0.03, p < 0.001), and altruism (b = -0.10, SE = 0.02, p < 0.001). Attention problems indirectly mediated the effect of insomnia on attitudes toward patients (99% CI = -0.10 [-0.16 to -0.05]), respect for autonomy (99% CI = -0.03 [-0.05 to -0.02]), holism (99% CI = -0.02 [-0.04 to -0.01]) empathy (99% CI = -0.03 [-0.04 to -0.01]), and altruism (99% CI = -0.02 [-0.03 to -0.01]). Conclusion: Nurses with insomnia-related attention problems are likely to have poor explicit social cognition such as attitude toward patients, altruism, empathy, respect for autonomy, and holism.

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images.

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Psychometric Validation of the Athens Insomnia Scale Among Nurses: A Robust Approach Using Both Classical Theory and Rating Scale Model Parameters.

Purpose: Few studies have investigated the validity of the Athens insomnia scale (AIS) using a robust approach of both classical theory and the rating scale model. Therefore, in this study, we investigated psychometric validation of the AIS using both of these approaches in nurses. Methods: Nurses (n= 563, age= 33.2±7.1 years) working in health facilities in Saudi Arabia participated in a cross-sectional study. Participants completed the AIS, socio-demographics tool, and sleep health-related questions. Results: Confirmatory factor analysis (CFA) favored a 2-factor structure with both comparative fit index (CFI), and incremental fit index (IFI) having values above 0.95. The 2-factor model had the lowest values of Akaike information criterion (AIC), root mean square error of approximation (RMSEA), χ 2, and χ 2/df. This 2-factor structure showed configural invariance (CFI more than 0.95, RMSEA less than 0.08, and Χ2/df less than 3), and metric, scalar, and strict invariance (based on Δ CFI ≤-0.01, and Δ RMSEA ≥ 0.015 criteria). No ceiling/floor effects were seen for the AIS total scores. Infit and outfit mean square values for all the items were within the acceptable range (<1.4, >0.6). The threshold estimates for each item were ordered as expected. Cronbach's α for the AIS tool, factor-1 score, factor-2 score was 0.86, 0.82, and 0.72, respectively. AIS factor scores-1/2 were significantly associated with a habitual feeling of tiredness after usual night sleep (p<0.001), Impairment of daytime socio-occupational functioning (p<0.05), and with a feeling of daytime fatigue, irritability, and restlessness (p<0.05). Conclusion: The findings favor the validity of a 2-factor structure of the AIS with adequate item properties, convergent validity, and reliability in nurses.

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images.

Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization-based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon's entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.

Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm.

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.

Extraction and application of keratin from natural resources: a review.

Over recent years, keratin has gained great popularity due to its exceptional biocompatible and biodegradable nature. It has shown promising results in various industries like poultry, textile, agriculture, cosmetics, and pharmaceutical. Keratin is a multipurpose biopolymer that has been used in the production of fibrous composites, and with necessary modifications, it can be developed into gels, films, nanoparticles, and microparticles. Its stability against enzymatic degradation and unique biocompatibility has found their way into biomedical applications and regenerative medicine. This review discusses the structure of keratin, its classification and its properties. It also covers various methods by which keratin is extracted like chemical hydrolysis, enzymatic and microbial treatment, dissolution in ionic liquids, microwave irradiation, steam explosion technique, and thermal hydrolysis or superheated process. Special emphasis is placed on its utilisation in the form of hydrogels, films, fibres, sponges, and scaffolds in various biotechnological and industrial sectors. The present review can be noteworthy for the researchers working on natural protein and related usage.

Wall Shear Stress Estimation of Thoracic Aortic Aneurysm Using Computational Fluid Dynamics.

An attempt has been made to evaluate the effects of wall shear stress (WSS) on thoracic aortic aneurysm (TAA) using Computational Fluid Dynamics (CFD). Aneurysm is an excessive localized swelling of the arterial wall due to many physiological factors and it may rupture causing shock or sudden death. The existing imaging modalities such as MRI and CT assist in the visualization of anomalies in internal organs. However, the expected dynamic behaviour of arterial bulge under stressed condition can only be effectively evaluated through mathematical modelling. In this work, a 3D aneurysm model is reconstructed from the CT scan slices and eventually the model is imported to Star CCM+ (Siemens, USA) for intensive CFD analysis. The domain is discretized using polyhedral mesh with prism layers to capture the weakening boundary more accurately. When there is flow reversal in TAA as seen in the velocity vector plot, there is a chance of cell damage causing clots. This is because of the shear created in the system due to the flow pattern. It is observed from the proposed mathematical modelling that the deteriorating WSS is an indicator for possible rupture and its value oscillates over a cardiac cycle as well as over different stress conditions. In this model, the vortex formation pattern and flow reversals are also captured. The non-Newtonian model, including a pulsatile flow instead of a steady average flow, does not overpredict the WSS (15.29 Pa compared to 16 Pa for the Newtonian model). Although in a cycle the flow behaviour is laminar-turbulent-laminar (LTL), utilizing the non-Newtonian model along with LTL model also overpredicted the WSS with a value of 20.1 Pa. The numerical study presented here provides good insight of TAA using a systematic approach to numerical modelling and analysis.