Early detection of Alzheimer's disease in structural and functional MRI.

Objectives: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.

Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data.

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques.

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis.

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.

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.

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.

Multi-stage classification of Alzheimer's disease from F-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.

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.

Evaluation of low bone mass and prediction of fracture risk using metacarpal radiogrammetry method: a comparative study with DXA and X-ray phantom.

Objectives: (i) To predict the future risk of osteoporotic fracture in women using a simple forearm radiograph. (ii) To assess osteoporosis in southern Indian women by using radiogrammetric technique in comparison with dual-energy X-ray absorptiometry (DXA) and X-ray phantom study.

Methods: The bone mineral density (BMD) of the right proximal femur by DXA and the X-ray measurements were acquired from the right forearm.

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.

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.

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.

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.

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.

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.