Pulmonology

Identifying mitochondria-related signatures for tuberculosis diagnosis through machine learning on single-cell transcriptomics data and experimental verification.

Tuberculosis (TB) remains a major cause of infectious disease mortality. Early diagnosis is crucial for curbing transmission and initiating timely treatment. However, the lack of reliable non-sputum-based diagnostic tools often delays prompt detection. Since mitochondrial dysregulation facilitates Mycobacterium tuberculosis (MTB) evasion, we explored mitochondria-related gene signatures as diagnos...

Parsimonious machine learning model to predict 1-year mortality and procedural futility after transcatheter aortic valve replacement.

BACKGROUND: Current risk scores inadequately predict long-term mortality after transcatheter aortic valve replacement (TAVR), limiting their ability to guide decisions around procedural futility. We aimed to develop and externally validate a machine learning (ML) model using only preprocedural variables to predict 1-year all-cause mortality. METHODS: An ML model was trained on a retrospective coho...

Artificial Intelligence in Asthma: From Diagnosis to Management.

Artificial intelligence (AI) offers new opportunities to improve asthma management across the care process. This review synthesizes evidence on AI app...

Deep learning based automated assessment of end-inspiratory pause maneuver reliability in invasive mechanical ventilation.

OBJECTIVE: Manual end-inspiratory pause maneuvers (EIPM) for plateau pressure (Pplat) measurement suffer from significant variability, yet no objectiv...

LobePrior segments lung lobes on computed tomography images in the presence of severe abnormalities.

The development of robust algorithms for lung and lobe segmentation is essential for diagnosing and monitoring pulmonary diseases. Obtaining manual or...

Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.

The existing TNM staging system provides insufficient prognostic information in gastric cancer (GC) patients. This study aims to establish a pathomics...

Thrombosis after surgical splenectomy - why, in whom and can we prevent it?

Splenectomy remains a common operation performed in the setting of trauma, haematological disease, malignancy and diagnostic purposes. Contemporary ev...

Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review.

Metabolic dysfunction-associated steatotic liver disease (MASLD) poses significant diagnostic challenges. We conducted a systematic review of artifici...

Excited-State-Guided Molecular Design System for Type I Photosensitizers.

Type I photosensitizers (PSs), which generate Type I reactive oxygen species (ROS) through the electron transfer pathway and remain effective under hy...

S3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network.

Convolutional Neural Networks (CNNs) have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial fea...

Explainable deep learning-based lung cancer diagnosis using clinically-guided local interpretable model-agnostic explanations.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, highlighting the urgent need for accurate and interpretable diagnost...

Development and validation of a prediction model for respiratory complications and in-hospital mortality in trauma patients.

Intraoperative video-based artificial intelligence model exceeding surgeon accuracy for predicting severe fibrosis in minimally invasive liver surgery.

Accurate evaluation of liver fibrosis is essential for surgical planning in patients with hepatocellular carcinoma (HCC), as advanced fibrosis or cirr...

Multimodal Learning with Privileged Report Supervision for Generalizable Tuberculosis Detection on Chest Radiographs.

K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.

Precise prediction of pneumonia remains a challenge mainly because effective integration of clinical data that are highly heterogeneous is mandatory. ...

Machine learning prediction of antibiotic resistance in Gram-negative pneumonia: a clinical-genomic integrated model.

Clinical evaluation of an AI-based pneumonia detection system on chest CT: a qualitative and quantitative analysis.

Artificial intelligence (AI) for early identification of radiotherapy related toxicities from the electronic health records of patients with head and neck cancer.

We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient elect...

Raman analysis of black carbon using artificial neural networks for emission source classification.

Black Carbon (BC), a major component of Particulate Matter (PM), plays a crucial role in air pollution, climate change, and public health impacts, due...

Artificial intelligence-enhanced three-dimensional radiological analysis for predicting spread through air spaces in early-stage lung adenocarcinoma.

OBJECTIVES: Spread through air space (STAS) is a pathological feature that correlates with poor prognosis, especially in patients with lung adenocarci...