Primary Care

Promoting smartphone-based keratitis screening using meta-learning: A multicenter study.

OBJECTIVE: Keratitis is the primary cause of corneal blindness worldwide. Prompt identification and referral of patients with keratitis are fundamental measures to improve patient prognosis. Although deep learning can assist ophthalmologists in automatically detecting keratitis through a slit lamp camera, remote and underserved areas often lack this professional equipment. Smartphones, a widely av...

Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.

Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this research, we aim to identify the risk factors that have an impact on MetS in the Bangladeshi population. Subsequently, we intend to construct predictive mac...

Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements.

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based obesity identification framework that integrates sm...

Comparison of Machine Learning Algorithms and Nomogram Construction for Diabetic Retinopathy Prediction in Type 2 Diabetes Mellitus Patients.

INTRODUCTION: The aim of this study was to compare various machine learning algorithms for constructing a diabetic retinopathy (DR) prediction model a...

Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular com...

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have...

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive tre...

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and p...

The early prediction of gestational diabetes mellitus by machine learning models.

BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with so...

Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

OBJECTIVE: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perin...

Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer.

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers canno...

Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.

The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung d...

The potential for large language models to transform cardiovascular medicine.

Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial i...

A deep convolutional neural network approach using medical image classification.

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to...

Obesity prediction: Novel machine learning insights into waist circumference accuracy.

AIMS: This study aims to enhance the precision of obesity risk assessments by improving the accuracy of waist circumference predictions using machine ...

A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks ...

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model.

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces ...

Identification and evaluation of deep foundation pit construction risks based on Grey-DEMATEL-Fuzzy comprehensive evaluation method.

In recent years, foundation pit construction has been rapidly developing in the direction of deep and large-scale, leading to the frequent occurrence ...

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of...

Artificial Intelligence and Cardiology Practice in Nigeria: Are We Ready?

Cardiovascular diseases are the leading cause of death globally. As cardiovascular risk factors continuously rise to pandemic levels, there is intense...