AI Medical Compendium Topic

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Lung

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BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, du...

Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique.

PloS one
Deep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by leveraging a large dataset conta...

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

JCI insight
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high di...

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration.

Biomedical physics & engineering express
This research presents an integrated framework designed to automate the classification of pulmonary chest x-ray images. Leveraging convolutional neural networks (CNNs) with a focus on transformer architectures, the aim is to improve both the accuracy...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Computers in biology and medicine
Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory condition...

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.

BMC medical imaging
OBJECTIVE: To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We cat...

Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.

International journal of chronic obstructive pulmonary disease
BACKGROUND AND PURPOSE: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on scree...

Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.

Physical and engineering sciences in medicine
Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in acad...

Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.

Journal of controlled release : official journal of the Controlled Release Society
There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapies is to restore normal fetal development and prevent irreversible conditions after birth. As a firs...

Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN.

Sensors (Basel, Switzerland)
Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing...