AIMC Topic: Radiography, Thoracic

Clear Filters Showing 521 to 530 of 626 articles

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
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
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
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
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
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...

Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.

Medicine
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom app...

Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.

Radiology
Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic diagnostic decision-making. Purpose To test whether the type of AI explanation and the correctness and...

DEEP LEARNING-BASED FRAMEWORK TO DETERMINE THE DEGREE OF COVID-19 INFECTIONS FROM CHEST X-RAY.

Georgian medical news
The corona virus disease-19 (COVID-19) epidemic, the whole globe is suffering from a medical condition catastrophe that is unprecedented in scale. As the coronavirus spreads, scientists are worried about offering or helping in the supply of remedies ...

Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.

Korean journal of radiology
OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.