Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81944
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
Glaucoma
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.
URI: https://hdl.handle.net/10356/81944
http://hdl.handle.net/10220/41055
DOI: 10.1136/amiajnl-2012-001336
Rights: © The Author(s) (published by Oxford University Press).
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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