Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81944
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dc.contributor.authorLiu, Jiangen
dc.contributor.authorZhang, Zhuoen
dc.contributor.authorWong, Damon Wing Keeen
dc.contributor.authorXu, Yanwuen
dc.contributor.authorYin, Fengshouen
dc.contributor.authorCheng, Junen
dc.contributor.authorTan, Ngan Mengen
dc.contributor.authorKwoh, Chee Keongen
dc.contributor.authorXu, Dongen
dc.contributor.authorTham, Yih Chungen
dc.contributor.authorAung, Tinen
dc.contributor.authorWong, Tien Yinen
dc.date.accessioned2016-08-03T08:54:05Zen
dc.date.accessioned2019-12-06T14:43:31Z-
dc.date.available2016-08-03T08:54:05Zen
dc.date.available2019-12-06T14:43:31Z-
dc.date.issued2013en
dc.identifier.citationLiu, 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.en
dc.identifier.urihttps://hdl.handle.net/10356/81944-
dc.description.abstractBackground - 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.en
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en
dc.format.extent7 p.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of the American Medical Informatics Associationen
dc.rights© The Author(s) (published by Oxford University Press).en
dc.subjectPatient dataen
dc.subjectMedical Retinal Imageen
dc.subjectMedical imaging informaticsen
dc.subjectGenome informationen
dc.subjectMultiple kernel learningen
dc.subjectGlaucomaen
dc.titleAutomatic glaucoma diagnosis through medical imaging informaticsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1136/amiajnl-2012-001336en
dc.identifier.pmid23538725-
item.grantfulltextnone-
item.fulltextNo Fulltext-
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