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Lung

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Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

Expert review of respiratory medicine
BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET res...

Unsupervised 3D Lung Segmentation by Leveraging 2D Segment Anything Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung segmentation is the first important step for lung nodule detection and lung cancer analysis. Deep neural networks have achieved state-of-the-art for most tasks in medical image analysis, including lung segmentation. However, training a deep lear...

Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thick...

Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis.

Journal of X-ray science and technology
ObjectiveThe goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, se...

Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics.

Journal of hazardous materials
Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for...

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Scientific reports
Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severit...

OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.

IEEE journal of biomedical and health informatics
The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-pre...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BMC medical imaging
BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonar...

Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.

Cardiovascular ultrasound
BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congest...

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation.

IEEE transactions on medical imaging
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the ...