Automatic glaucoma diagnosis through medical imaging informatics.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Liu, J; Zhang, Z; Wong, DWK; Xu, Y; Yin, F; Cheng, J; Tan, NM; Kwoh, CK; Xu, D; Tham, YC; Aung, T; Wong, TY

Published Date

  • November 2013

Published In

Volume / Issue

  • 20 / 6

Start / End Page

  • 1021 - 1027

PubMed ID

  • 23538725

Pubmed Central ID

  • 23538725

Electronic International Standard Serial Number (EISSN)

  • 1527-974X

Digital Object Identifier (DOI)

  • 10.1136/amiajnl-2012-001336

Language

  • eng

Conference Location

  • England