Atopy

Deep learning to estimate lung disease mortality from chest radiographs.

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained us...

Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss.

Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal model for human hearing, while presenting a large database of speech and noise sounds. We first used manifold learning to identify the neural subspace i...

A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status.

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the...

Deep learning for detection of drug hypersensitivity reactions.

Imaging-derived biomarkers in Asthma: Current status and future perspectives.

Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the ...

Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study.

New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children's home and artificial intelligen...

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).

Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leuko...

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma.

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated...

Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders.

Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental...

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome.

DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there a...

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS ...

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respir...

AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature.

MOTIVATION: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environ...

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach.

In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing ...

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test f...

Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.

To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using st...

S. aureus and IgE-mediated diseases: pilot or copilot? A narrative review.

INTRODUCTION: is a major opportunistic pathogen that has been implicated in the pathogenesis of several chronic inflammatory diseases including bronc...

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.

BACKGROUND: Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are...

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization.

OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. T...

Effect Evaluation of Electronic Health PDCA Nursing in Treatment of Childhood Asthma with Artificial Intelligence.

Asthma in children has a long duration and is prone to recurring attacks. Children will feel chest tightness, shortness of breath, cough, and difficul...