A Medical Decision Support Platform for Identifying Thrombospondin 1 in Non-alcoholic Fatty Liver Disease via Immuno-Infiltration Analysis.

Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1).

Advancements in non-invasive microwave brain stimulation: A comprehensive survey.

This survey provides a comprehensive insight into the world of non-invasive brain stimulation and focuses on the evolving landscape of deep brain stimulation through microwave research. Non-invasive brain stimulation techniques provide new prospects for comprehending and treating neurological disorders. We investigate the methods shaping the future of deep brain stimulation, emphasizing the role of microwave technology in this transformative journey.

Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing.

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system.

A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks.

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions.

A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct.

Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic.

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques.

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI).

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO.

Congenital Generalized Lipodystrophy (CGL) is a rare autosomal recessive disease characterized by near complete absence of functional adipose tissue from birth. CGL diagnosis can be based on clinical data including acromegaloid features, acanthosis nigricans, reduction of total body fat, muscular hypertrophy, and protrusion of the umbilical scar. The identification and knowledge of CGL by the health care professionals is crucial once it is associated with severe and precocious cardiometabolic complications and poor outcome.

ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System.

Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems existing in the design phases, based on a probabilistic approach, by applying the Monte Carlo method (MCM). With this approach, it is feasible to identify the dominant contributing factors of imprecision in the evaluated system.

Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis.

Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal.

Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data.

Parkinson's disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer.

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.

Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use.

An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation.

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection.

Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification.

The COVID-19 pandemic continues to wreak havoc on the world's population's health and well-being. Successful screening of infected patients is a critical step in the fight against it, with radiology examination using chest radiography being one of the most important screening methods. For the definitive diagnosis of COVID-19 disease, reverse-transcriptase polymerase chain reaction remains the gold standard.

Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection.

Atrial fibrillation (AF) is a serious medical condition of the heart potentially leading to stroke, which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial Intelligence of Things (AIoT) enable smart abnormality detection by analyzing streaming healthcare data from the sensor end of users. Analyzing streaming data in the cloud leads to challenges of response latency and privacy issues, and local inference by a model deployed on the user end brings difficulties in model update and customization.

Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG.

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people's emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications.

Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing.

Due to the rapid development of artificial intelligence technology, industrial sectors are revolutionizing in automation, reliability, and robustness, thereby significantly increasing quality and productivity. Most of the surveillance and industrial sectors are monitored by visual sensor networks capturing different surrounding environment images. However, during tempestuous weather conditions, the visual quality of the images is reduced due to contaminated suspended atmospheric particles that affect the overall surveillance systems.

Multi-Class Skin Lesion Detection and Classification via Teledermatology.

Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas.

A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system.

Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia.

Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines.

Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals.

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.

Efficient Security and Authentication for Edge-Based Internet of Medical Things.

Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content.

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson's Disease from Dynamic Handwriting Analysis.

In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson's disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson's disease patients acquired here are made available to further contribute to research related to this topic.