Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advan...

Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier di...

A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries signifi...

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning alg...

Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.

BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouri...

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significa...

Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.

This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classi...

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach.

BACKGROUND: In oncology, electronic health records contain textual key information for the diagnosis...

Automated classification of mandibular canal in relation to third molar using CBCT images.

BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) b...

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and m...

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristic...

Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay...

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.

INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequen...

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

OBJECTIVE: To explore the differences and associations of hypoxic parameters among distinct types of...

Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.

The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing...

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

BACKGROUND: With the rapid increase of chest computed tomography (CT) images, the workload faced by ...

A deep convolutional neural network approach using medical image classification.

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of ...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to ...

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