Primary Care

Deep learning for early dental caries detection in bitewing radiographs.

The early detection of initial dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched, with promising performance. In this study, we developed a CNN model using a U-shaped...

Prediction Algorithm of Young Students' Physical Health Risk Factors Based on Deep Learning.

Young people's physical and mental health is the foundation of society's overall development and the key to improving people's health quality. Middle school students' physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students cau...

Deep Learning for Classifying Physical Activities from Accelerometer Data.

Physical inactivity increases the risk of many adverse health conditions, including the world's major non-communicable diseases, such as coronary hear...

Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although...

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.

In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional r...

Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.

BACKGROUND: Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detectio...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as pos...

Improving stock trading decisions based on pattern recognition using machine learning technology.

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machi...

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs whe...

DeepUWF: An Automated Ultra-Wide-Field Fundus Screening System via Deep Learning.

The emerging ultra-wide field of view (UWF) fundus color imaging is a powerful tool for fundus screening. However, manual screening is labor-intensive...

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.

This study investigated the diagnostic performance, feasibility, and end-user experiences of an artificial intelligence (AI)-assisted diabetic retinop...

A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings.

Monitoring the indoor environment of historic buildings helps to identify potential risks, provide guidelines for improving regular maintenance, and p...

Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.

In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for di...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and ...

Artificial Intelligence in Dermatology: A Threat or an Opportunity?

The worldwide explosion of interest in artificial intelligence (AI) has created a before-and-after moment in our lives by generating great improvement...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pne...

Data-driven identification of complex disease phenotypes.

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches ...

Virtual Reality-Based Framework to Simulate Control Algorithms for Robotic Assistance and Rehabilitation Tasks through a Standing Wheelchair.

The implementation of control algorithms oriented to robotic assistance and rehabilitation tasks for people with motor disabilities has been of increa...

Deep Learning for Diabetes: A Systematic Review.

Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabet...

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning.

This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evalu...