Recent advancements in non-invasive wearable electrochemical biosensors for biomarker analysis - A review.

A biomarker is a molecular indicator that can be used to identify the presence or severity of a disease. It may be produced due to biochemical or molecular changes in normal biological processes. In some cases, the presence of a biomarker itself is an indication of the disease, while in other cases, the elevated or depleted level of a particular protein or chemical substance aids in identifying a disease. Biomarkers indicate the progression of the disease in response to therapeutic interventions. Identifying these biomarkers can assist in diagnosing the disease early and providing proper therapeutic treatment. In recent years, wearable electrochemical (EC) biosensors have emerged as an important tool for early detection due to their excellent selectivity, low cost, ease of fabrication, and improved sensitivity. There are several challenges in developing a fully integrated wearable sensor, such as device miniaturization, high power consumption, incorporation of a power source, and maintaining the integrity and durability of the biomarker for long-term continuous monitoring. This review covers the recent advancements in the fabrication techniques involved in device development, the types of sensing platforms utilized, different materials used, challenges, and future developments in the field of wearable biosensors.

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images.

Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study's goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups.

Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique.

The aims and objectives of the study were to i) perform image segmentation using a color-based k-means clustering algorithm and feature extraction using binary robust invariant scalable key points (BRISK), maximum stable extremal regions (MSER), features from accelerated segment test (FAST), Harris, and orientated FAST and rotated BRIEF (ORB); ii) compare the performance of classical machine learning techniques such as LogitBoost, Bagging, and SVM with a quantum machine learning technique. For the proposed study, 240 hand thermal images were acquired in the dorsal view and ventral view of both the right and left-hand regions of RA and normal subjects. The hot spot regions from the thermograms were segmented using a color-based k-means clustering technique. The features from the segmented hot spot region were extracted using different feature extraction methods. Finally, normal and RA groups were categorized using LogitBoost, Bagging, and support vector machine (SVM) classifiers. The proposed study used two testing methods, such as 10-fold cross-validation and a percentage split of 80-20%. The LogitBoost classifier outperformed with an accuracy of 93.75% using the 10-fold cross-validation technique compared to other classifiers. Also, the quantum support vector machine (QSVM) obtained a prediction accuracy of 92.7%. Furthermore, the QSVM model reduces the computational cost and training time of the model to classify the RA and normal subjects. Thus, thermograms with classical machine learning and quantum machine learning algorithms could be considered a feasible technique for classifying normal and RA groups.

Multi-stage classification of Alzheimer's disease from 18F-FDG-PET images using deep learning techniques.

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), and Alzheimer's disease (AD) from Cognitively Normal (CN), and assess the results. 18F-FDG PET imaging modality for brain were procured from Alzheimer's disease neuroimaging initiative's (ADNI) repository. The ResNet50V2 model layers were utilised for feature extraction, with the final convolutional layers fine-tuned for this dataset's multi-classification objectives. Multiple metrics and feature maps were utilized to scrutinize and evaluate the model's statistical and qualitative inference. The multi-classification model achieved an overarching accuracy of 98.44% and Area under the receiver operating characteristic curve of 95% on the testing set. Feature maps aided in deducing finer aspects of the model's overall operation. This framework helped classifying from the 18F-FDG PET brain images, the subtypes of Mild Cognitive Impairment (MCI) which include EMCI, LMCI, from AD, CN groups and achieved an all-inclusive sensitivity of 94% and specificity of 95% respectively.

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers.

Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

Computer-based automated analysis of X-ray and thermal imaging of knee region in evaluation of rheumatoid arthritis.

The aim and objectives of the study are as follows: (1) to perform automated segmentation of knee X-ray images using fast greedy snake algorithm and feature extraction using gray level co-occurrence matrix method, (2) to implement automated segmentation of knee thermal image using RGB segmentation method and (3) to compare the features extracted from the segmented knee region of X-ray and thermal images in rheumatoid arthritis patients using a biochemical method as standard. In all, 30 rheumatoid arthritis patients and 30 age- and sex-matched healthy volunteers were included in the study. X-ray and thermography images of knee regions were acquired, and biochemical tests were carried out subsequently. The X-ray images were segmented using fast greedy snake algorithm, and feature extractions were performed using gray level co-occurrence matrix method. The thermal image was segmented using RGB-based segmentation method and statistical features were extracted. Statistical features extracted after segmentation from X-ray and thermal imaging of knee region were correlated with the standard biochemical parameters. The erythrocyte sedimentation rate shows statistically significant correlations (p < 0.01) with the X-ray parameters such as joint space width and % combined cortical thickness. The skin surface temperature measured from knee region of thermal imaging was highly correlated with erythrocyte sedimentation rate. Among all the extracted features namely mean, variance, energy, homogeneity and difference entropy depict statistically significant percentage differences between the rheumatoid arthritis and healthy subjects. From this study, it was observed that thermal infrared imaging technique serves as a potential tool in the evaluation of rheumatoid arthritis at an earlier stage compared to radiography. Hence, it was predicted that thermal imaging method has a competency in the diagnosis of rheumatoid arthritis by automated segmentation methods.

Computer-based measurements of joint space analysis using metacarpal morphometry in hand radiograph for evaluation of rheumatoid arthritis.

AIM AND OBJECTIVES: The aim and objectives are as follows: (i) to perform an automated segmentation of the hand from radiographs using a dual tree complex wavelet-based watershed algorithm; ii) to compare the measured statistical features of the joint space of the hand using gray level co-occurrence matrix (GLCM) method with standard diagnostic parameters of rheumatoid arthritis (RA). METHODS: Fifty-three patients with RA and 17 age- and sex-matched healthy controls were included in the study. The erythrocyte sedimentation rate (ESR), C-reactive protein, rheumatoid factor, health assessment questionnaire score (HAQ), disease activity score (DAS) and hand radiographs of all the subjects were obtained. Joint space width and cortical thickness were measured in metacarpophalangeal joints (MCP) and metacarpal bone semi-automatically using MIMICS software. Dual tree complex wavelet transform-based watershed algorithm was applied for automated segmentation, and feature extraction was performed using the GLCM method in hand radiographs of the total population. RESULTS: In the RA group (n = 53), the joint space width measured in the MCP1, MCP3, MCP5 of the hand were reduced significantly (P < 0.01) by 16.4%, 15.6%, and 17.5%, respectively compared to the normal group (n = 17). The measured combined cortical thickness at the second, third and fourth metacarpal bones of the hand were reduced significantly (P < 0.01) by 9.5%, 12% and 8%, respectively in the RA group compared to the normal group. CONCLUSION: The dual tree complex wavelet transform-based watershed algorithm provided effective segmentation in the digitized hand radiographs. The standard diagnostic parameters for RA were highly correlated with measured statistical features at MCP3 hand joint in the total population studied.

Ultrasound Color Doppler Image Segmentation and Feature Extraction in MCP and Wrist Region in Evaluation of Rheumatoid Arthritis.

The present study focuses on automatically to segment the blood flow pattern of color Doppler ultrasound in hand region of rheumatoid arthritis patients and to correlate the extracted the statistical features and color Doppler parameters with standard parameters. Thirty patients with rheumatoid arthritis (RA) and their total of 300 joints of both the hands, i.e., 240 MCP and 60 wrists were examined in this study. Ultrasound color Doppler of both the hands of all the patients was obtained. Automated segmentation of color Doppler image was performed using color enhancement scaling based segmentation algorithm. The region of interest is fixed in the MCP joints and wrist of the hand. Features were extracted from the defined ROI of the segmented output image. The color fraction was measured using Mimics software. The standard parameters such as HAQ score, DAS 28 score, and ESR was obtained for all the patients. The color fraction tends to be increased in wrist and MCP3 joints which indicate the increased blood flow pattern and color Doppler activity as part of inflammation in hand joints of RA. The ESR correlated significantly with the feature extracted parameters such as mean, standard deviation and entropy in MCP3, MCP4 joint and the wrist region. The developed automated color image segmentation algorithm provides a quantitative analysis for diagnosis and assessment of RA. The correlation study between the color Doppler parameters with the standard parameters provides moral significance in quantitative analysis of RA in MCP3 joint and the wrist region.

Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis.

The aim of the study was (1) to perform an automated segmentation of hot spot regions of the hand from thermograph using the k-means algorithm and (2) to test the potential of features extracted from the hand thermograph and its measured skin temperature indices in the evaluation of rheumatoid arthritis. Thermal image analysis based on skin temperature measurement, heat distribution index and thermographic index was analyzed in rheumatoid arthritis patients and controls. The k-means algorithm was used for image segmentation, and features were extracted from the segmented output image using the gray-level co-occurrence matrix method. In metacarpo-phalangeal, proximal inter-phalangeal and distal inter-phalangeal regions, the calculated percentage difference in the mean values of skin temperatures was found to be higher in rheumatoid arthritis patients (5.3%, 4.9% and 4.8% in MCP3, PIP3 and DIP3 joints, respectively) as compared to the normal group. k-Means algorithm applied in the thermal imaging provided better segmentation results in evaluating the disease. In the total population studied, the measured mean average skin temperature of the MCP3 joint was highly correlated with most of the extracted features of the hand. In the total population studied, the statistical feature extracted parameters correlated significantly with skin surface temperature measurements and measured temperature indices. Hence, the developed computer-aided diagnostic tool using MATLAB could be used as a reliable method in diagnosing and analyzing the arthritis in hand thermal images.

Evaluation of complete Freund's adjuvant-induced arthritis in a Wistar rat model. Comparison of thermography and histopathology.

OBJECTIVE: The aim of this study was to evaluate the progression of arthritis in a complete Freund's adjuvant (CFA)-induced Wistar rat model and to monitor inflammatory arthritis activity using thermal imaging compared with histopathology. METHODOLOGY: Fifteen adult male Wistar rats were studied in an adjuvant-induced arthritis model by the injection of complete Freund's adjuvant in the right hind limb and right forelimb, respectively, with the left limbs used as controls. Thermal image analysis based on skin temperature measurement, radiographic analysis based on erosion, limb circumference measurement, and histopathological evaluation were performed. RESULTS: The average skin temperature difference on the arthritis-induced side and control for both the forelimb and hind limb were found to be 1.09 °C and 0.98 °C, respectively. Pearson correlation analysis revealed that skin surface temperature was positively correlated with the arthritis severity score (forelimb: r = 0.64, hind limb: r = 0.66, p < 0.05).A significant correlation also existed between thermal imaging temperature and visual scoring of X-ray (forelimb: r = 0.56, hind limb: r = 0.67, p < 0.05). CONCLUSION: Thermal imaging measurements correlated with arthritis severity score, radiological score, and ankle diameters. Hence, thermography could be used to diagnose and analyze inflammatory activity of arthritis at the preclinical stage.

Evaluation of functional ability of rheumatoid arthritis based on HAQ score and BMD among South Indian patients.

Aim of this study is to analyze the functional ability of rheumatoid arthritis among South Indian male and female patients based on HAQ score and forearm ulna-BMD measurement by peripheral DXA, and to investigate the correlation between forearm ulna-BMD and HAQ score among RA patients. Sixty-four patients with RA and 64 age- and sex-matched healthy controls were included in this study. The health assessment questionnaire test was self administered by each RA patients. The bone mineral density (BMD) in forearm ulna region was measured using peripheral Dual energy X-ray absorptiometry (osteometer model-DTX200 Meditech.Inc, Hawthorn, California, USA) both for RA patients and for healthy control group. RA patients (n = 64) and age- and sex-matched healthy controls (n = 64) were selected, of which 46 (72%) patients were women and 18 (28%) were men. The mean age was 47.75 ± 11.37 years, and a majority of the patients were in the age group of 30-75 years. The mean age of healthy controls was 46.42 ± 10.67 years. For male RA patients, U-BMD shows moderate significance with healthy controls (0.371 ± 0.05 (g cm(2)) [mean ± SD], 0.413 ± 0.05 (g cm(2)), P = 0.03). For female RA patients, U-BMD was highly significant with that of healthy controls (0.300 ± 0.132 (g cm(2)), 0.376 ± 0.05 (g cm(2)), P = 0.0006). Because as U-BMD decreases for RA patients, HAQ score increases, hence, Pearson correlation analysis revealed that U-BMD was negatively correlated with HAQ score (r = -0.732, P < 0.0001). Forearm U-BMD for RA patients is significantly lower than the healthy controls both for male and for female patients. There was a negative correlation found between HAQ score and P-DXA forearm U-BMD.