Pulmonology

Deep learning model for pathological invasiveness prediction using smartphone-based surgical resection images in clinical stage IA lung adenocarcinoma (SuRImage): a prospective, multicentric, diagnostic study.

BACKGROUND: In clinical stage IA lung adenocarcinoma (LUAD), rapid and accurate intraoperative diagnosis is crucial to decide whether to perform segmentectomy and lobectomy. Frozen section analysis is time consuming and not always reliable for LUAD diagnosis and grading. We developed deep learning models using surgical resection images to assist in prompt diagnosis and risk stratification of stage...

Patient-Specific Cardio-Respiratory Model for Optimization of Cardiac Radioablation.

Stereotactic Arrhythmia Radioablation (STAR) is a promising treatment for refractory ventricular tachycardia. However, its precision may be hampered by cardiac and respiratory motions. Multiple techniques exist to mitigate the effects of these displacements. The purpose of this work was, based on cardiac and respiratory dynamic CT scans, to generate a patient-specific dynamic model of the structur...

Predictive Value of Machine Learning for Mortality Risk in Acute Pancreatitis: A Systematic Review and Meta-Analysis.

BACKGROUND: Acute pancreatitis (AP), a common acute abdominal disease, has a high mortality rate in severe cases. Accurate mortality prediction is cru...

Deep Learning Segmentation of Pectoralis Muscle Volume at CT and Comparison with Pectoralis Muscle Area in COPD.

Purpose To develop a deep learning model for segmenting pectoralis muscle volume (PMV) at CT and evaluate the reproducibility, group differences, and ...

A Unified Deep Learning Framework for Liver MR Elastography Postprocessing: Proof-of-Concept Study.

Magnetic resonance elastography (MRE) enables non-invasive quantification of liver stiffness and plays a pivotal role in the assessment of hepatic fib...

Fetal pHocus: A Novel Approach to Non-Invasive Fetal Arterial Blood pH Assessment via Near-Infrared Spectroscopy.

Modern intrapartum fetal health assessments are currently limited to monitoring heart rate and spatial parameters, neglecting critical biomarkers that...

Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation.

When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized mo...

Predicting ventilation from single breathing phase non-contrast CT using Swin Transformers.

BACKGROUND: Pulmonary ventilation imaging enables functional avoidance radiotherapy treatment plans by quantifying regional lung function. However, cu...

Pushing the limits of spatial resolution in clinical PCD-CT using a dedicated high-resolution convolutional neural network (HR-CNN).

BACKGROUND: Photon-counting-detector (PCD) CT systems offer ultra-high spatial resolution, yet the visual spatial resolution on clinical images often ...

A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease.

Accurate, non-invasive liver fibrosis detection is essential for chronic liver disease management, particularly with rising metabolic dysfunction-asso...

Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.

BACKGROUND: Critically ill patients generate large volumes of complex data, creating challenges for timely clinical decision making in intensive care ...

DIGITAL SOLUTIONS FOR TUBERCULOSIS SURVEILLANCE AND CONTROL IN PRIMARY HEALTH CARE: A SCOPING REVIEW.

Digital solutions are essential for eliminating tuberculosis as a public health problem. They can be applied across all stages of patient care, health...

Artificial Intelligence-Assisted Ultrasound in Austere Trauma Environments: Implications for Role 1 Military Medicine.

Pararescue jumpers are United States Air Force medical tactical operators who provide advanced trauma and prolonged casualty care in austere, high-ris...

Federated Learning Framework for Privacy-Preserving Explainable AI-Driven Clinical Decision-Making.

The application of artificial intelligence (AI) in clinical diagnostics has shown substantial potential; however, conventional centralized learning fr...

3D deep learning model to predict the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection: a multicenter retrospective cohort study.

The purpose of this study was to investigate the efficacy of a three-dimensional (3D) deep learning (DL) model in predicting recurrence risk of stage ...

[Pulmonary embolism response team (PERT): development, implementation, and clinical impact].

Pulmonary embolism (PE) is a common and potentially fatal venous thromboembolic disease. Traditional management paradigms, often characterized by insu...

Predictive accuracy of a perioperative hemodynamic indices-based prediction model for moderate-to-severe acute kidney injury after orthotopic heart transplantation.

BACKGROUND: Acute kidney injury is a common complication after orthotopic heart transplantation. Previous models have failed to consider the impact of...

Transformer-based super-resolution lung CT images improve visualization of multiple diseases.

OBJECTIVES: To assess the Transformer-based Swin2SR model for super-resolution (SR) enhancement of lung CT images and its clinical potential. METHODS:...

Revisiting the NETs-CYBB-p38 MAPK axis in bronchiectasis: The underlying oxidative injury mechanism.

BACKGROUND: Bronchiectasis is a chronic neutrophilic respiratory disease frequently complicated by Pseudomonas aeruginosa (P. aeruginosa) infection. N...

Imaging Modalities in Tuberculosis.

Tuberculosis (TB) remains a major global health challenge, with increasing prevalence of multidrug-resistant and extrapulmonary forms complicating dia...