Pulmonology

Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.

RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detection and localization in CXRs.

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.

BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant role in activating the long-chain fatty acids.

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

BACKGROUND: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim...

Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review.

Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields with far-reaching implications for various biomedica...

An integrated approach to a predictive and ranking model of use error using fuzzy BWM and fuzzy TOPSIS.

Avoiding error in handling artifacts is crucial for achieving a high level of system reliability and safety assessment. This study develops a predicti...

Pulmonary Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.

RATIONALE AND OBJECTIVES: Hyperpolarized Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of Xe signal-void to the anatomic H MRI thoraci...

CT ventilation images produced by a 3D neural network show improvement over the Jacobian and HU DIR-based methods to predict quantized lung function.

BACKGROUND: Radiation-induced pneumonitis affects up to 33% of non-small cell lung cancer (NSCLC) patients, with fatal pneumonitis occurring in 2% of ...

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence.

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and expl...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revasculari...

Role of Robotics in Image-Guided Trans-Arterial Interventions.

The integration of robotic systems in image-guided trans-arterial interventions has revolutionized the field of Interventional Radiology (IR), offerin...

Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by...

Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding.

BACKGROUND: The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute r...

Artificial intelligence in respiratory pandemics-ready for disease X? A scoping review.

OBJECTIVES: This study aims to identify repeated previous shortcomings in medical imaging data collection, curation, and AI-based analysis during the ...

Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.

BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long...

Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.

There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanc...

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -the recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules...

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.

BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose c...

Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.

OBJECTIVES: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding infe...

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making t...

Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

PURPOSE: The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging...