Pulmonology

Engineering biomarker representations of vital signs data enhances deep learning mortality prediction.

OBJECTIVES: We evaluated bidirectional long short-term memory models for predicting inpatient mortality using different approaches to processing vital signs data collected during the initial 24 h of intensive care unit (ICU) admissions. MATERIALS AND METHODS: We compared 3 vital-sign representations: (1) raw data recorded every 5 min, (2) preprocessed data averaged hourly, and (3) preprocessed dat...

Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.

BACKGROUND: Respiratory epidemics often place substantial pressure on intensive care units (ICU), which are continuously challenged to managing acute and life-threatening conditions under unpredictable workloads. During these periods, ICUs usually exhibit inefficient patient flows, treatment delays, and critical resource shortages. Proactive decision-making and precise interventions are therefore ...

Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.

Non-invasive health monitoring has recently gained a lot of consideration in the modern healthcare system, because it has the potential to diagnose di...

Artificial intelligence on chest X-ray for tuberculosis screening in Tanzania: a multicentre evaluation.

Tanzania has adopted artificial intelligence (AI)-assisted chest X-ray screening for tuberculosis (TB), including the use of CAD4TB version 6, which i...

A systematic review of machine learning approaches to bovine tuberculosis in cattle.

Bovine tuberculosis (bTB) is a chronic zoonotic disease, caused by Mycobacterium bovis which despite years of eradication attempts, is still prevalent...

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-caus...

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 rec...

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....

IML-UNet: A brain-inspired spatiotemporal collaborative encoding method for medical image sequence registration.

BACKGROUND AND OBJECTIVE: Deformable medical image registration is important for radiotherapy planning, respiratory motion analysis, and organ functio...

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...