AIMC Topic: Radiography, Thoracic
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Does the deep learning-based iterative reconstruction affect the measuring accuracy of bone mineral density in low-dose chest CT?
The British journal of radiology
Jun 1, 2025
OBJECTIVES: To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT.
Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.
Aging clinical and experimental research
May 13, 2025
BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk,...
A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration.
Scientific data
May 3, 2025
This dataset, Collab-CXR, provides a unique resource to study human-AI collaboration in chest X-ray interpretation. We present experimentally generated data from 227 professional radiologists who assessed 324 historical cases under varying informatio...
Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population.
Radiology. Artificial intelligence
May 1, 2025
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest ...
Artificial Intelligence Assessment of Chest Radiographs for COVID-19.
Clinical lymphoma, myeloma & leukemia
May 1, 2025
BACKGROUND: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; how...
Development and Evaluation of an Automated Protocol Recommendation System for Chest CT Using Natural Language Processing With CLEVER Terminology Word Replacement.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
May 1, 2025
To evaluate the clinical performance of a Protocol Recommendation System (PRS) automatic protocolling of chest CT imaging requests. 322 387 consecutive historical imaging requests for chest CT between 2017 and 2022 were extracted from a radiology i...
Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.
Radiation protection dosimetry
Apr 22, 2025
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...
Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.
Radiology
Mar 1, 2025
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...
Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study.
Radiology
Mar 1, 2025
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...
AI Applications for Thoracic Imaging: Considerations for Best Practice.
Radiology
Feb 1, 2025
Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thoracic imaging show cumulative evidence of effectiveness. These include AI assistance for reading chest...
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