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Tear film breakup time-based dry eye disease detection using convolutional neural network

  • S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics
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Abstract

Dry eye disease (DED) is a chronic eye disease and a common complication among the world's population. Evaporation of moisture from tear film or a decrease in tear production leads to an unstable tear film which causes DED. The tear film breakup time (TBUT) test is a common clinical test used to diagnose DED. In this test, DED is diagnosed by measuring the time at which the first breakup pattern appears on the tear film. TBUT test is subjective, labour-intensive and time-consuming. These weaknesses make a computer-aided diagnosis of DED highly desirable. The existing computer-aided DED detection techniques use expensive instruments for image acquisition which may not be available in all eye clinics. Moreover, among these techniques, TBUT-based DED detection techniques are limited to finding only tear film breakup area/time and do not identify the severity of DED, which can essentially be helpful to ophthalmologists in prescribing the right treatment. Additionally, a few challenges in developing a DED detection approach are less illuminated video, constant blinking of eyes in the videos, blurred video, and lack of public datasets. This paper presents a novel TBUT-based DED detection approach that detects the presence/absence of DED from TBUT video. In addition, the proposed approach accurately identifies the severity level of DED and further categorizes it as normal, moderate or severe based on the TBUT. The proposed approach exhibits high performance in classifying TBUT frames, detecting DED, and severity grading of TBUT video with an accuracy of 83%. Also, the correlation computed between the proposed approach and the Ophthalmologist's opinion is 90%, which reflects the noteworthy contribution of our proposed approach.

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Acknowledgements

The authors would sincerely like to thank Dr. Ravish Kinkhabwala (M.D. OPHTH, AIIMS, New Delhi) from Vaikunth Eye Clinic, Ahmedabad, Gujarat, India, for providing valuable information and knowledge on the dry eye disease and related concepts along with the TBUT video dataset. We are also thankful to him for helping in the process of video frame annotation and for preparing ground truths for our research.

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Authors and Affiliations

  1. Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

    Aditi Haresh Vyas & Mayuri A. Mehta

  2. Symbiosis Center for Applied Artificial Intelligence and Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University, Pune, India

    Ketan Kotecha

  3. Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, Maharashtra, India

    Sharnil Pandya

  4. College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT, 0810, Australia

    Mamoun Alazab

  5. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Thippa Reddy Gadekallu

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  1. Aditi Haresh Vyas
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Vyas, A.H., Mehta, M.A., Kotecha, K. et al. Tear film breakup time-based dry eye disease detection using convolutional neural network. Neural Comput & Applic 36, 143–161 (2024). https://doi.org/10.1007/s00521-022-07652-0

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