AI Medical Compendium Topic

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Radiography, Thoracic

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Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.

Radiology
Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent examinations like chest radiograph interpretation. Purpose To develop and evaluate the diagnostic accur...

Advancing Chest X-ray Diagnostics via Multi-Modal Neural Networks with Attention.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The healthcare field is undergoing a profound shift, with deep learning in AI increasingly augmenting medical expertise in complex and challenging tasks. Our research addresses the challenging task of chest X-ray image diagnostics, a field characteri...

Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
As the COVID-19 pandemic has put a strain on healthcare systems around the world, accurate and rapid virus detection has become increasingly important. Lung issues caused by COVID-19 can be detected using a chest X-ray (CXR). In order to automaticall...

Efficient Lung Segmentation from Chest Radiographs using Transfer Learning and Lightweight Deep Architecture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung delineation constitutes a critical preprocessing stage for X-ray-based diagnosis and follow-up. However, automatic lung segmentation from chest radiographs (CXR) poses a challenging problem due to anatomical structures' varying shapes and sizes,...

Fast Rule-based NER in SpaCy for Chest Radiography Reports with CheXpert's 14 Categories.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chest X-ray imaging is widely used in medical examinations, with 2 billion performed globally annually. Interpreting X-ray images is time-consuming, prompting the development of AI-assisted systems. Generating a large set of high-quality labeled imag...

Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Inhalation therapy is the predominant method of treatment for a variety of respiratory diseases. The effectiveness of such treatment is dependent on the accuracy of medication delivery. Thus, personalized inhalation therapy wherein inhaler designs ar...

Classification of pulmonary diseases from chest radiographs using deep transfer learning.

PloS one
Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Chest radiographs are commonly used to diagnose pulmonary diseases. In clinical practice, diagnosing p...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

BMC medical imaging
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly...

Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study.

Radiology
Background Multimodal generative artificial intelligence (AI) technologies can produce preliminary radiology reports, and validation with reader studies is crucial for understanding the clinical value of these technologies. Purpose To assess the clin...

Forecasting trends of rising emergency department chest imaging using machine learning.

Emergency radiology
INTRODUCTION: Imaging studies in the acute care setting, such as the emergency room, have been increasing. In this report, we use the Centers for Medicare and Medicaid services (CMS) database to assess trends in ED chest CT and chest CTA imaging in E...