Primary Care

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance.

BACKGROUND AND AIMS: Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research.

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge.

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical c...

Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies.

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative f...

The future of valvular heart disease assessment and therapy.

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Seri...

Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs.

PURPOSE: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has b...

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, su...

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A mor...

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.

Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment wi...

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead comp...

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. Thi...

Improving newborn screening in India: Disease gaps and quality control.

In India, newborn screening (NBS) is essential for detecting health problems in infants. Despite significant progress, significant gaps and challenges...

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases...

Predicting hypertension control using machine learning.

Hypertension is a widely prevalent disease and uncontrolled hypertension predisposes affected individuals to severe adverse effects. Though the import...

Screening/diagnosis of pediatric endocrine disorders through the artificial intelligence model in different language settings.

UNLABELLED: This study is aimed at examining the impact of ChatGPT on pediatric endocrine and metabolic conditions, particularly in the areas of scree...

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncol...

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed.

This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users' heartbeats on a smart bed. Ta...

Can large language models replace humans in systematic reviews? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages.

Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs...

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the co...

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective app...

Predictive value of ultrasonic artificial intelligence in placental characteristics of early pregnancy for gestational diabetes mellitus.

BACKGROUND: To explore the predictive value of placental features in early pregnancy for gestational diabetes mellitus (GDM) using deep and radiomics-...