Please use this identifier to cite or link to this item:
Title: Automatic glaucoma diagnosis through medical imaging informatics
Authors: Liu, Jiang
Zhang, Zhuo
Wong, Damon Wing Kee
Xu, Yanwu
Yin, Fengshou
Cheng, Jun
Tan, Ngan Meng
Kwoh, Chee Keong
Xu, Dong
Tham, Yih Chung
Aung, Tin
Wong, Tien Yin
Keywords: Patient data
Medical Retinal Image
Medical imaging informatics
Genome information
Multiple kernel learning
Issue Date: 2013
Source: Liu, J., Zhang, Z., Wong, D. W. K., Xu, Y., Yin, F., Cheng, J., et al. (2013). Automatic glaucoma diagnosis through medical imaging informatics. Journal of the American Medical Informatics Association, 20(6), 1021-1027.
Series/Report no.: Journal of the American Medical Informatics Association
Abstract: Background - Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. Objective - To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. Materials and methods - 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Results and discussion - Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. Conclusions - AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.
DOI: 10.1136/amiajnl-2012-001336
Schools: School of Computer Engineering 
Rights: © The Author(s) (published by Oxford University Press).
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 10

Updated on Jul 15, 2024

Web of ScienceTM
Citations 10

Updated on Oct 24, 2023

Page view(s) 50

Updated on Jul 20, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.