Pulmonology

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). However, uncertainty in identification of radiologic UIP pattern leads to the continued need for invasive surgical biopsy. We thus developed and validate...

Evaluation of the cytotoxic effect of titanium dioxide nanoparticles in human embryonic lung cells.

BACKGROUND/AIM: Titanium dioxide nanoparticles are widely used in a variety of products, including sunscreens, paints, and ceramics. However, their increasing use has raised concerns about their potential health risks. Titanium dioxide nanoparticles have been shown to have the ability to enter the bloodstream and accumulate in various tissues, reaching the fetus via the placenta. The aim of this s...

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

PURPOSE: The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qana...

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to re...

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.

We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CX...

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with ...

Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.

Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests,...

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.

DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understandi...

CTFlow: Mitigating Effects of Computed Tomography Acquisition and Reconstruction with Normalizing Flows.

Mitigating the effects of image appearance due to variations in computed tomography (CT) acquisition and reconstruction parameters is a challenging in...

Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance.

PURPOSE: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomograph...

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.

OBJECTIVES: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of mod...

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques.

Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asth...

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drive...

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and progno...

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed ...

DeepHistoNet: A robust deep-learning model for the classification of hepatocellular, lung, and colon carcinoma.

In recent days, non-communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer...

Deep learning-based lung sound analysis for intelligent stethoscope.

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-l...

Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract.

PROBLEM: Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting ora...

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and developm...

Extracting lung contour deformation features with deep learning for internal target motion tracking: a preliminary study.

. To propose lung contour deformation features (LCDFs) as a surrogate to estimate the thoracic internal target motion, and to report their performance...