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Lung

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Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

AJR. American journal of roentgenology
Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropr...

Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location.

European journal of radiology
PURPOSE: Computer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evaluated the benefits and pitfalls of CAD in the real world. This study aimed to evaluate the independen...

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Sensors (Basel, Switzerland)
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combin...

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Scientific reports
Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adop...

A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy.

Physics in medicine and biology
. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdominal cancers. Current real-time motion management strategies require dedicated systems that are not ava...

Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.

Journal of computer assisted tomography
OBJECTIVES: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD).

Deep learning-based age estimation from chest CT scans.

International journal of computer assisted radiology and surgery
PURPOSE: Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on the...

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

Medical physics
BACKGROUND: The main application of [18F] FDG-PET ( FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FL...

A systematic approach to deep learning-based nodule detection in chest radiographs.

Scientific reports
Lung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the detection a...

Redefining Lobe-Wise Ground-Glass Opacity in COVID-19 Through Deep Learning and its Correlation With Biochemical Parameters.

IEEE journal of biomedical and health informatics
During COVID-19 pandemic qRT-PCR, CT scans and biochemical parameters were studied to understand the patients' physiological changes and disease progression. There is a lack of clear understanding of the correlation of lung inflammation with biochemi...