Chan, Lawrence Wing-Chi, Chan, Phoebe Suk-Tak, Zheng, Yongping, Wong, Alex Ka-Shing, Liu, Ying ![]() |
Abstract
Multi-disciplinary platform is created to store and integrate DICOM objects from various clinical disciplines. With artificial intelligence, clinical decision support system is built to assess risk of disease complications using the features extracted from the DICOM objects and their interrelationship. Diabetes Mellitus is considered as the disease of interest and the risks of its complications are assessed based on the extracted features. The synergy of the DICOM-based multi-disciplinary platform and the clinical decision support system provides promising functions for extracting and interrelating consistent features of clinical information.
Item Type: | Book Section |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Subjects: | R Medicine > R Medicine (General) T Technology > TA Engineering (General). Civil engineering (General) |
Publisher: | Springer |
ISBN: | 9783540782964 |
ISSN: | 1860949X |
Related URLs: | |
Last Modified: | 25 Oct 2022 08:03 |
URI: | https://orca.cardiff.ac.uk/id/eprint/51234 |
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