Author Identifier (ORCID)
Syed Zulqarnain Gilani
https://orcid.org/0000-0002-7448-2327
Naeha Sharif
https://orcid.org/0000-0001-8351-6288
David Suter
Abstract
More than 55,000 people world-wide die from Cardiovascular Disease (CVD) each day. Calcification of the abdominal aorta is an established marker of asymptomatic CVD. It can be observed on scans taken for vertebral fracture assessment from Dual Energy X-ray Absorptiometry machines. Assessment of Abdominal Aortic Calcification (AAC) and timely intervention may help to reinforce public health messages around CVD risk factors and improve disease management, reducing the global health burden related to CVDs. Our research addresses this problem by proposing a novel and reliable framework for automated “fine-grained” assessment of AAC. Inspired by the vision-to-language models, our method performs sequential scoring of calcified lesions along the length of the abdominal aorta on DXA scans; mimicking the human scoring process.
Document Type
Conference Proceeding
Date of Publication
1-1-2022
Volume
13433 LNCS
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer
School
Nutrition and Health Innovation Research Institute / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science
RAS ID
46965
Funders
National Health and Medical Research Council
Grant Number
NHMRC Number : APP1183570
First Page
439
Last Page
450
Comments
This is an author's accepted manuscript of:
Gilani, S. Z., Sharif, N., Suter, D., Schousboe, J. T., Reid, S., Leslie, W. D., & Lewis, J. R. (2022). Show, attend and detect: Towards fine-grained assessment of abdominal aortic calcification on vertebral fracture assessment scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 13433 (pp. 439-450). Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_42