Pulmonology

The association of systemic inflammatory indices with all-cause mortality risks in patients with COPD: A cohort study based on machine learning.

This study aimed to comprehensively assess the prognostic value of routinely obtained blood-based systemic inflammatory indices in predicting all-cause mortality among individuals with chronic obstructive pulmonary disease (COPD). This retrospective cohort study analyzed data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007-2010. A total of 1109 eligible adults with C...

Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite recent therapeutic advances, including immune checkpoint inhibitors and emerging targeted agents, survival outcomes remain poor. Recent molecular insights have identified four transcription factor-driven subtypes-SCLC-A, SCLC-N, SCLC-P, and the inflamed...

AlveolEye: Rapid and precise lung morphometry guided by computer vision.

Rigorous and reproducible evaluation of lung tissue under different conditions is necessary to interpret development, injury, and pharmacologic interv...

Data-driven outcome prediction and rational resource allocation in severe traumatic brain injury: a multicenter retrospective study integrating survival analysis and machine learning ensembles.

INTRODUCTION: Severe traumatic brain injury (sTBI), is a leading cause of death and disability among young and middle-aged populations worldwide. OBJE...

Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection.

OBJECTIVE: Accurate detection of adventitious respiratory sounds, such as wheezes and crackles, is essential for diagnosing and managing respiratory c...

An Electromagnetic Navigation Surgical Robotic System (ENSRS) for Transthoracic Puncture of Small Pulmonary Nodules.

OBJECTIVE: To address the limitations of traditional CT-guided pulmonary nodule interventions, such as excessive radiation exposure, prolonged procedu...

Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.

This study introduces a domain-conditioned and temporally guided diffusion framework for accelerated dynamic MRI reconstruction, in which the reverse ...

Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.

BACKGROUND & AIMS: Transient elastography (TE) is routinely undertaken for non-invasive assessment of liver fibrosis and steatosis, but is limited by ...

A Hybrid-Mamba network with dual level attention fusion for multimodal COPD diagnosis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) remains difficult to diagnose reliably due to limitations of conventional spirometry and CT i...

Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.

BACKGROUND: ST-elevation myocardial infarction (STEMI) exhibits substantial clinical heterogeneity complicating prehospital risk stratification. Tradi...

Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.

BACKGROUND: The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy....

A deep learning lung cancer segmentation pipeline to facilitate CT-based radiomics.

AIM: CT-based radio-biomarkers could provide non-invasive insights into tumour biology to risk-stratify patients. One of the limitations is the labori...

Machine learning method for the prediction of Bedaquiline-resistant Mycobacterium tuberculosis.

The study addresses the increasing resistance to the FDA-approved drug Bedaquiline (BDQ) in Mycobacterium tuberculosis (MTB). The absence of any defin...

Integrating statistical and machine learning approaches to characterise and model the performance of a full-scale wastewater treatment plant.

Wastewater treatment plants operate under highly variable influent conditions, challenging process control and regulatory compliance. This study propo...

Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.

BACKGROUND: Tuberculosis-diabetes mellitus (TB-DM) multimorbidity significantly increases the risk of multidrug-resistant/rifampicin-resistant tubercu...

Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.

Lung cancer persists as the predominant oncological cause of mortality globally, underscoring an imperative public health issue that demands effective...

CT-based body composition and its change through time in relation to outcomes in participants screened for lung cancer.

BACKGROUND: Computed Tomography (CT) scans allow opportunistic evaluation of body composition. We investigated whether body composition and change thr...

Early ctDNA stratifies survival in locally advanced and oligometastatic lung cancer treated with radiotherapy.

PURPOSE: Circulating tumor fraction estimate (ctFE) is a machine learning-derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothe...

Choroidal vascularity across aging and the spectrum of age-related macular degeneration: an AI-based OCT study.

PURPOSE: To characterize the choroidal morphology across a spectrum of participants affected by normal aging as well as age-related macular degenerati...

Deep-learning based quantitative evaluation of postoperative atelectasis following right upper lobectomy.

Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning-based framework to ...