Pulmonology

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

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Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format.

To improve respiratory gating accuracy and radiation treatment throughput, we developed a generalize...

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung c...

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment.

PURPOSE: The health and economic consequences of artificial intelligence (AI) systems for mechanical...

Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases With an Artificial Intelligence Contextual Framework.

BACKGROUND & AIMS: Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis, Crohn's ...

Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques.

BACKGROUND AND OBJECTIVES: Lifestyle-related diseases (LSDs) impose a substantial economic burden on...

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the he...

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

BACKGROUND: Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, ...

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and divers...

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey.

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus...

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using ...

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable...

Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

OBJECTIVE: To investigate the effect of uncertainty estimation on the performance of a Deep Learning...

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.

PURPOSE: Radiotherapy planning incorporating functional lung images has the potential to reduce pulm...

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms.

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, em...

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respi...

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible...

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tool...

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