Pulmonology

Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals and provides a rich source of data for informative analyses. This study examines the cardiac surgery ICU, where the vital topic of patient ventilation ...

DKCN-Net: Deep kronecker convolutional neural network-based lung disease detection with federated learning.

In the healthcare field, lung disease detection techniques based on deep learning (DL) are widely used. However, achieving high stability while maintaining privacy remains a challenge. To address this, this research employs Federated Learning (FL), enabling doctors to train models without sharing patient data with unauthorized parties, preserving privacy in local models. The study introduces the D...

Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning.

Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. Howeve...

ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Tuberculosis is a high-mortality infectious disease. Manual sputum smear microscopy is a common and effective method for screening tuberculosis. Howev...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classifica...

Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COV...

A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.

BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management.

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO may improve neurologic...

Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been ide...

Application of artificial intelligence and machine learning in bovine respiratory disease prevention, diagnosis, and classification.

Bovine respiratory disease (BRD) is the leading infectious disease in cattle, resulting in significant economic losses and welfare concerns in beef an...

Multifunctional Fiber Robotics with Low Mechanical Hysteresis for Magnetic Navigation and Inhaled Gas Sensing.

Recently, increasing research attention has been directed toward detecting the distribution of hazardous gases in the respiratory system for potential...

Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.

INTRODUCTION: Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for act...

Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .

Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multid...

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availabi...

Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.

Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML)...

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early cl...

KL-DNAS: Knowledge Distillation-Based Latency Aware-Differentiable Architecture Search for Video Motion Magnification.

Video motion magnification is the task of making subtle minute motions visible. Many times subtle motion occurs while being invisible to the naked eye...

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are ma...

Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.

This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (...