Pulmonology

Use of Artificial Intelligence to Predict Outcomes in Critically Ill Patients Receiving Extracorporeal Membrane Oxygenation: A Systematic Review.

Extracorporeal membrane oxygenation (ECMO) is widely used in patients with severe cardiac or respiratory failure. ECMO is resource and cost intensive and carries the potential for significant complications; therefore, identifying patients who would derive the most benefit and personalizing its application are important. A number of scoring systems have previously been developed to predict the outc...

Dual-Peptide Nanoplatform: Mesoporous Silica Nanoparticles Functionalized With a Cell-Penetrating Peptide and Loaded With Rationally Designed Antimicrobial Peptides for Tuberculosis Therapy.

Tuberculosis remains a major infectious disease worldwide and demands new therapeutic approaches capable of targeting intracellular Mycobacterium tuberculosis. Using a machine learning (ML)-guided design strategy based on the plectasin scaffold, a peptide library was generated and DC05 was identified as the most active analog. As a free peptide, DC05 inhibited the H37Rv strain with an MIC90 of 12....

IGSF9 drives malignant transformation and predicts early-stage lung adenocarcinoma in integrated transcriptomic analyses.

PURPOSE: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), often presents with mild or absent symptoms in its...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung Cancer Recurrence.

PURPOSE: Interpretability is highly desirable for oncologic outcome prediction, as it increases the level of transparency and trustworthiness of the m...

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.

Chest radiography remains an essential diagnostic tool for thoracic diseases, yet interpreting overlapping anatomical structures is particularly chall...

A cough sound-based deep learning algorithm for accessible prompt detection of chronic obstructive pulmonary disease with smartphones.

Early COPD diagnosis is vital for effective management, yet conventional tools such as professional spirometers are often inaccessible in resource-lim...

Artificial intelligence and multi-omic empowered-tracing macrophage diagnostic and druggable implications in coronavirus pneumonia.

BACKGROUND: As the innate immune barrier, macrophages play an important role in coronavirus infection pneumonia and post-infection regeneration. OBJEC...

Optimal machine learning models and factors for global infectious diseases: An ecological analysis.

BACKGROUND: Infectious diseases are a major global public health challenge. Socioeconomic and environmental changes have complicated the situation, un...

Pulmo: Patient Education in Pulmonary Hypertension via Artificial Intelligence-Based Digital Characters.

Proteomic profiling and pathway analyses reveal molecular signatures and immune networks in pediatric sepsis.

BACKGROUND: Sepsis remains a leading cause of childhood mortality worldwide. Most deaths occur within the first few days of presentation, underscoring...

Personalized mapping of body homeostasis using whole-body PET connectomics and routine FDG PET imaging.

BACKGROUND: Immuno-inflammation and systemic alterations are key features of chronic diseases. While PET molecular imaging is widely used in precision...

Construction and validation of high altitude pulmonary edema prediction models based on deep learning and quantitative analysis of X-ray images.

Spinal morphology-based multimodal AI for predicting pulmonary dysfunction in adolescent idiopathic scoliosis.

A machine learning-based prediction model for treatment efficacy in smear and/or chest X-ray positive tuberculosis patients.

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.

BACKGROUND: Accurate and rapid diagnosis is essential for controlling pulmonary tuberculosis (PTB) by enabling timely intervention and reducing diseas...

Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI Reconstruction.

BACKGROUND: Hyperpolarized 129Xe MRI faces technical challenges including low signal-to-noise ratio and breath-hold constraints. Current literature fo...

Combined use of serum KL-6 and respiratory functional parameters for identifying fibrotic lung damage in SARS-CoV-2-induced interstitial pneumonia.

BACKGROUND: Post-COVID19 pulmonary fibrosis (PCPF) has been reported in a significant proportion of patients who survive the acute SARS-CoV-2 infectio...

Visual question answering-based image-finding generation for pulmonary nodules on chest CT from structured annotations.

PURPOSE: Interpretation of imaging findings based on morphological characteristics is important for diagnosing pulmonary nodules on chest computed tom...

Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis.

BACKGROUND: Artificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiol...