Pulmonology

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. We created an algorithm to predict cMRI-quantified PRF from echocardiography using machine learning (ML). We retrospectively performed echocardiograph...

Evaluating artificial intelligence responses to respiratory medicine questions.

Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports.

BACKGROUND: Smell disorders are commonly reported with COVID-19 infection. The smell-related issues associated with COVID-19 may be prolonged, even af...

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obsta...

Predicting drug-Protein interaction with deep learning framework for molecular graphs and sequences: Potential candidates against SAR-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regard...

Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in c...

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation.

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast sc...

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used...

Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.

OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis ...

A reliable deep-learning-based method for alveolar bone quantification using a murine model of periodontitis and micro-computed tomography imaging.

OBJECTIVES: This study focuses on artificial intelligence (AI)-assisted analysis of alveolar bone for periodontitis in a mouse model with the aim to c...

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤...

Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design.

The COVID-19 pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates a profound understanding of the virus and ...

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the ...

Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery.

Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data f...

RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning.

BACKGROUND: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pha...

Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation.

Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, sq...

Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollut...

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and i...

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction.

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only pro...