Pulmonology

Development and external validation of interpretable machine learning models for personalized multiple treatment recommendations in non-small cell lung cancer.

OBJECTIVE: Treatment decision-making for non-small cell lung cancer (NSCLC) is complex, necessitating individualized decision-support tools to improve prognosis. This study aimed to develop and externally validate interpretable machine learning models to predict multiple treatment recommendations (surgery, radiotherapy and chemotherapy) and assess their prognostic implications. METHODS: We utilize...

A Simplified and Data-Driven Lung Ultrasound Approach for Predicting Surfactant Need in Preterm Infants: A Pilot Study Using Machine Learning and Rule-Based Models.

OBJECTIVES: Six-region lung ultrasound (LUS) scores show good predictive value for predicting surfactant need in preterm infants but rely on a fixed threshold, which may lead to misclassification near the cut-off and lack data-driven justification for selecting these 6 regions. This study explored whether evaluating individual regions-and combinations-could improve predictive accuracy and utility....

Hospital-acquired pneumonia: Past, present, and perspectives.

Hospital-acquired pneumonia (HAP) remains the most frequent and lethal hospital acquired infection, driving ICU mortality, prolonged length of stay, a...

Breaking the bottleneck of asthma treatment: the future of omni-targeted therapy.

BACKGROUND: Bronchial asthma is a complex, highly heterogeneous disease involving multiple pathological mechanisms and inflammatory pathways. Traditio...

Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules.

OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatm...

Evaluation of image quality in pediatric portable chest radiographs using AI-based noise reduction and edge enhancement.

PURPOSE: To evaluate the image quality of pediatric portable chest radiographs processed using a deep learning-based noise reduction (NR) algorithm im...

Consensus position statements for the standardized application of histological grading and staging systems in MASH clinical trials.

BACKGROUND & AIMS: Assessment of histologic disease activity (grading) and fibrosis (staging) is a prerequisite for patient selection and evaluation o...

Leveraging the ADAM10 prodomain for selective inhibition to enhance recovery after myocardial infarction.

BACKGROUND AND PURPOSE: Inflammation plays a crucial role in the development and progression of numerous acute and chronic diseases such as myocardial...

Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.

PURPOSE: Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related gen...

Predicting neurobehavioral outcomes in people with HIV.

BACKGROUND: This study identified complex, multidimensional, longitudinal biopsychosocial (BPS) phenotypes (MLBPSPs) in people with HIV (PWH) and eval...

Interpretable artificial intelligence to predict preoperative risk factors for failure to rescue after coronary artery bypass grafting.

OBJECTIVE: Failure to rescue (FTR) is a significant quality indicator for postoperative cardiothoracic care. We developed an interpretable artificial ...

Spatial architecture of development and disease.

Tissue architecture is a product of a multilayered molecular landscape, where even subtle disruptions in the spatial context can initiate or reflect d...

Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning.

PURPOSE: The purpose of this study was to evaluate the relationship between structural abnormalities on CT and lung function prior to and after initia...

Machine learning approaches to predict drug resistance in tuberculosis.

Tuberculosis (TB) remains a global health crisis, with 10.8 million cases and 1.25 million deaths in 2023. The rise of drug-resistant TB has complicat...

A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.

Cystic bronchiectasis and pneumonia are respiratory conditions that significantly impact morbidity and mortality worldwide. Diagnosing these diseases ...

A novel Convolutional Shuffle Attention Xtreme Gradient Boost Network for improved lung cancer detection using computed tomography images.

Lung cancer is a severe and life-threatening type of cancer that originates in the lung tissues. Computed Tomography (CT) image emerges as the primary...

Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.

Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional ener...

Automated detection of neonatal pulmonary hypertension in echocardiograms with a deep learning model.

BACKGROUND: In infants, pulmonary hypertension (PH) increases morbidity and mortality. Echocardiography, though standard, is time- and expertise-deman...

Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography.

The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretat...

Imaging in chronic thromboembolic pulmonary hypertension: review of the current literature.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe, life-threatening complication of pulmonary embolism with pulmonary hypertension (PH...