Pulmonology

Accuracy of artificial intelligence-based simulation for assessing lung vessels and volume using unenhanced computed tomography.

OBJECTIVES: The advantages of preoperative three-dimensional (3D) image simulations, which require enhanced computed tomography (ECT), for anatomical lung resection are well documented. However, the necessity for contrast agent presents a significant barrier for some patients. This study thus aims to evaluate the accuracy of an artificial intelligence-based 3D simulation using unenhanced computed ...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that bridge the genetics and clinical manifestations of human disease. However, the genetic architecture of these AI endophenotypes remains largely unexplored in the context of human multiorgan system diseases. Using publicly available genome-wide association...

Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagn...

Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks

According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this i...

Development of a Deep Learning Model for the Prediction of Ventilator Weaning

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficul...

Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA

Medical imaging plays a pivotal role in modern healthcare, with computed tomography pulmonary angiography (CTPA) being a critical tool for diagnosin...

Camera Measurement of Blood Oxygen Saturation

Blood oxygen saturation (SpO2) is a crucial vital sign routinely monitored in medical settings. Traditional methods require dedicated contact sensor...

An Efficient Approach to Detecting Lung Nodules Using Swin Transformer

Lung cancer has the highest rate of cancer-caused deaths, and early-stage diagnosis could increase the survival rate. Lung nodules are common indica...

Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.

BACKGROUND: We use real-world data to develop a lung cancer screening (LCS) eligibility mechanism that is both accurate and free from racial bias.

Development of an Unpaired Deep Neural Network for Synthesizing X-ray Fluoroscopic Images from Digitally Reconstructed Tomography in Image Guided Radiotherapy

Purpose The purpose of this study was to develop and evaluate a deep neural network (DNN) capable of generating flat-panel detector (FPD) images fro...

Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging.

OBJECTIVES: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival hei...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires techn...

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.

Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bron...

Bridging the Gap in Neonatal Care: Evaluating AI Chatbots for Chronic Neonatal Lung Disease and Home Oxygen Therapy Management.

OBJECTIVE: To evaluate the accuracy and comprehensiveness of eight free, publicly available large language model (LLM) chatbots in addressing common q...

A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.

This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform n...

Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study.

Background Multimodal generative artificial intelligence (AI) technologies can produce preliminary radiology reports, and validation with reader studi...

Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.

BACKGROUND: Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate t...

Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.

Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent exa...

Segmenting Bi-Atrial Structures Using ResNext Based Framework

Atrial fibrillation (AF) is the most common cardiac arrhythmia, significantly contributing to mortality, particularly in older populations. While pu...

Boundary-Emphasized Weight Maps for Distal Airway Segmentation

Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like le...