Publications by authors named "Naeha Sharif"
Article Synopsis
- Abdominal aortic calcification (AAC) is linked to higher cardiovascular risks and poor long-term outcomes, and can be assessed using a scoring method on bone density screening images.
- Researchers developed a machine-learning algorithm (ML-AAC24) to automate AAC scoring, making it easier to use compared to manual assessments.
- In a study of 1,023 older women, results showed that those with moderate to extensive AAC had a significantly higher risk of experiencing fractures and hospitalization from falls over a decade-long follow-up.
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise.
View Article and Find Full Text PDFBackground: Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training.
View Article and Find Full Text PDF