Primary Care

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation.

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisu...

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, w...

Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics.

Intelligent health diagnosis for young children aims at maintaining and promoting the healthy development of young children, aiming to make young chil...

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems.

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. ...

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions.

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory...

Sample Size Calculation for Clinical Trials of Medical Decision Support Systems with Binary Outcome.

Currently, software products for use in medicine are actively developed. Among them, the dominant share belongs to clinical decision support systems (...

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the id...

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration.

Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wea...

Arterial Hypertension and the Hidden Disease of the Eye: Diagnostic Tools and Therapeutic Strategies.

Hypertension is a major cardiovascular risk factor that is responsible for a heavy burden of morbidity and mortality worldwide. A critical aspect of c...

Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image...

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extr...

Validation of a Predictive Model for New Baseline Renal Function After Radical Nephrectomy or Robot-Assisted Partial Nephrectomy in Japanese Patients.

The study's aim was to externally validate a new predictive model for the new baseline glomerular filtration rate (NB-GFR) postnephrectomy among Japa...

Smart Sensorization Using Propositional Dynamic Logic.

The current high energy prices pose a serious challenge, especially in the domestic economy. In this respect, one of the main problems is obtaining do...

Obesity and individual performance: the case of eSports.

BACKGROUND/OBJECTIVES: The study considers the problem of the inclusion of people with obesity in the context of the growing role of computer-based wo...

Artificial intelligence in virtual screening: Models versus experiments.

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Exper...

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques.

OBJECTIVES: We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overwei...

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diag...

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of stron...

(De)troubling transparency: artificial intelligence (AI) for clinical applications.

Artificial intelligence (AI) and machine learning (ML) techniques occupy a prominent role in medical research in terms of the innovation and developme...

Continuous-time probabilistic models for longitudinal electronic health records.

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML)...