Pulmonology

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States.

Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilit...

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.

OBJECTIVES: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB).

Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet.

Accurate lung tumor segmentation has great significance in the treatment planning of lung cancer. However, robust lung tumor segmentation becomes chal...

Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications.

In the modern world, Tuberculosis (TB) is regarded as a serious health issue with a high rate of mortality. TB can be cured completely by early diagno...

Computer aided detection of tuberculosis using two classifiers.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with...

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

INTRODUCTION: Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that resi...

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction mo...

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.

BACKGROUND: Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in ...

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. ...

Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns...

Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach.

Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HD...

Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and con...

CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes.

According to the standard guidelines by the FDA, process validation in biopharma manufacturing encompasses a life cycle consisting of three stages: pr...

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

OBJECTIVE: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. Acco...

A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.

This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time moni...

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLL...

COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number o...

[Robotic Left Hepatectomy Using the Glissonean Pedicle Approach for the Treatment of Caroli's Syndrome].

BACKGROUND: Caroli's syndrome is a rare disease characterised by non-obstructive dilation of intrahepatic bile ducts, hepatic fibrosis, and an increas...

Interventional Pulmonary.

Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model.

Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during he...