Pulmonology

Impact of Deep Learning-Based Computer-Aided Detection and Electronic Notification System for Pneumothorax on Time to Treatment: Clinical Implementation.

OBJECTIVE: To assess whether the implementation of deep learning (DL) computer-aided detection (CAD) that screens for suspected pneumothorax (PTX) on chest radiography (CXR) combined with an electronic notification system (ENS) that simultaneously alerts both the radiologist and the referring clinician would affect time to treatment (TTT) in a real-world clinical practice.

Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.

BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom and other drugs in clinical trials. We aimed to validate and re-optimize known NITs, and most importantly to develop new machine learning (ML)-based N...

Comprehensive review of dysphagia and technological advances in dysphagia food.

As the global population ages, dysphagia is becoming increasingly common among the elderly, posing serious risks such as choking, aspiration pneumonia...

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruse...

Predicting Asthma Exacerbations Using Machine Learning Models.

INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis ar...

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.

BACKGROUND: Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The prese...

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.

BACKGROUND: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by p...

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer.

BACKGROUND: The anti-angiogenic drugs showed remarkable efficacy in the treatment of lung cancer. Nonetheless, the potential roles of the intra-tumora...

Deep learning-assisted surface-enhanced Raman spectroscopy detection of intracellular reactive oxygen species.

Realizing the intelligent analysis of the intracellular reactive oxygen species (ROS) is beneficial to quick diagnosis of diseases. Herein, surface-en...

Identification and validation of biomarkers related to mitochondria during ex vivo lung perfusion for lung transplants based on machine learning algorithm.

BACKGROUND: Ex vivo lung perfusion (EVLP) is a critical strategy to rehabilitate marginal donor lungs, thereby increasing lung transplantation (LTx) r...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in b...

Small Molecule Inhibitors of Topoisomerase I Identified by Machine Learning and In Vitro Assays.

Tuberculosis (TB) caused by is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasing...

BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis.

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Co...

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) i...

Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to det...

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Many studies have shown that infectious diseases are responsible for the majority of deaths in children under five. Among these children, Acute Respir...

A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis.

Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the...

SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection.

OBJECTIVES: By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolism has been improved, leading to more accurate iden...

Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations.

PROBLEM: Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Au...

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct hist...