Journal of Medical Systems

, 42:227 | Cite as

An Automated Grading and Diagnosis System for Evaluation of Dry Eye Syndrome

  • Ayşe BağbabaEmail author
  • Baha Şen
  • Dursun Delen
  • Betül Seher Uysal
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


This article describes methods used to determine the severity of Dry Eye Syndrome (DES) based on Oxford Grading Schema (OGS) automatically by developing and applying a decider model. The number of dry punctate dots occurred on corneal surface after corneal fluorescein staining can be used as a diagnostic indicator of DES severity according to OGS; however, grading of DES severity exactly by carefully assessing these dots is a rather difficult task for humans. Taking into account that current methods are also subjectively dependent on the perception of the ophtalmologists coupled with the time and resource intensive requirements, enhanced diagnosis techniques would greatly contribute to clinical assessment of DES. Automated grading system proposed in this study utilizes image processing methods in order to provide more objective and reliable diagnostic results for DES. A total of 70 fluorescein-stained cornea images from 20 patients with mild, moderate, or severe DES (labeled by an ophthalmologist in the Keratoconus Center of Yildirim Beyazit University Ataturk Training and Research Hospital) used as the participants for the study. Correlations between the number of dry punctate dots and DES severity levels were determined. When automatically created scores and clinical scores were compared, the following measures were observed: Pearson’s correlation value between the two was 0.981; Lin’s Concordance Correlation Coefficients (CCC) was 0.980; and 95% confidence interval limites were 0.963 and 0.989. The automated DES grade was estimated from the regression fit and accordingly the unknown grade is calculated with the following formula: Gpred = 1.3244 log(Ndots) - 0.0612. The study has shown the viability and the utility of a highly successful automated DES diagnostic system based on OGS, which can be developed by working on the fluorescein-stained cornea images. Proper implemention of a computationally savvy and highly accurate classification system, can assist investigators to perform more objective and faster DES diagnoses in real-world scenerios.


Dry eye Oxford grading scale Image processing Fluorescein staining Corneal images 


Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.” (26,379,996/218, 15.11.2017, Yildirim Beyazit University, Ankara).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Engineering DepartmentAnkara Yıldırım Beyazıt UniversityAnkaraTurkey
  2. 2.Center for Health Systems Innovation, Spears School of Business, Department of Management Science and Information SystemsOklahoma State UniversityTulsaUSA
  3. 3.Atatürk Education and Research HospitalAnkaraTurkey

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