Pulmonology

Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.

Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment of chemicals, predicting chronic in vivo nanotoxicity remains challenging due to the intricate interactions at multiple interfaces like nano-biofluids...

Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.

BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increases hospitalization costs. This study aimed to develop a predictive model for delirium in this patient population and identify associated risk factors.

Performance evaluation of reduced complexity deep neural networks.

Deep Neural Networks (DNN) have achieved state-of-the-art performance in medical image classification and are increasingly being used for disease diag...

Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.

Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its clinical features overlapping with those of other com...

Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.

BACKGROUND: Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoi...

Artificial intelligence in cardiovascular magnetic resonance imaging.

Artificial intelligence is rapidly evolving and its possibilities are endless. Its primary applications in cardiac magnetic resonance imaging have foc...

Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.

Lung cancer (LC) is a leading cause of cancer-related fatalities worldwide, underscoring the urgency of early detection for improved patient outcomes....

ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification.

Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory dise...

Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a Tertiary Center.

BACKGROUND: Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate manageme...

Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.

BACKGROUND: Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive r...

A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to ov...

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projec...

Classification of pulmonary diseases from chest radiographs using deep transfer learning.

Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Ch...

Artificial intelligence challenge of discriminating cutaneous arteritis and polyarteritis nodosa based on hematoxylin-and-eosin images of skin biopsy specimens.

Diseases that develop necrotizing vasculitis of cutaneous muscular arteries include cutaneous arteritis (CA) and polyarteritis nodosa (PAN). It is dif...

Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration.

In this study, the biological activities of the extracts obtained under optimum extraction conditions of the kernel part of Juglans regia L. were dete...

A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.

Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine resp...

Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

OBJECTIVES: Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fi...

Subphenotyping prone position responders with machine learning.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify s...

Machine learning prediction of right ventricular volume and ejection fraction from two-dimensional echocardiography in patients with pulmonary regurgitation.

Right ventricular (RV) end-diastolic volume (RVEDV) and ejection fraction (RVEF) by cardiac MRI (cMRI) guide management in chronic pulmonary regurgita...

Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.

OBJECTIVES: To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models u...