RATIONALE AND OBJECTIVES: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 12, 2024
Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use...
PURPOSE: Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothes...
Current problems in diagnostic radiology
Dec 10, 2024
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...
Journal of the American Heart Association
Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
RATIONALE AND OBJECTIVES: We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
Journal of medical engineering & technology
Dec 9, 2024
The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the ...
The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by ...