title: Automatic glaucoma diagnosis through medical imaging informatics.
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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Ocular Imaging, Institute for Infocomm Research, Singapore, Singapore.
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Zhang
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.forename: Yanwu
authors.initials: Y
authors.lastname: Xu
authors.forename: Fengshou
authors.initials: F
authors.lastname: Yin
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.forename: Ngan Meng
authors.initials: NM
authors.lastname: Tan
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.forename: Yih Chung
authors.initials: YC
authors.lastname: Tham
authors.forename: Tin
authors.initials: T
authors.lastname: Aung
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging; D003941:Diagnostic Techniques, Ophthalmological; D005260:Female; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D008297:Male; D012372:ROC Curve; D012160:Retina / Q000473:pathology; D012680:Sensitivity and Specificity; D012984:Software
keywords: Genome information; Medical Retinal Image; glaucoma; medical imaging informatics; multiple kernel learning; patient data
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