An Automated Tear Film Break Time based Dry Eye Disease Diagnosis using Deep Learning
Deep Learning has been rapidly adopted in various spheres of healthcare for translating biomedical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis. Amongst the several successfully developed healthcare applications, in this case study, I would like to discuss how commonly occurring Dry Eye Disease (DED) can be diagnosed accurately and speedily using deep learning based automated system!
DED is one of the commonly occurring chronic disease in the world today. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patient. Certain factors such as prolonged use of electronic gadgets, old age, environmental conditions, medication, smoking habits and use of contact lens can disturb the tear film balance and can lead to evaporation of moisture from tear film which causes dry eye disease. If DED is left untreated, it can cause infection, corneal ulcer or blindness. However, diagnosis of dry eye is a difficult task because it occurs due to different factors. An ophthalmologist sometimes requires multiple tests or repetitive tests for proper diagnosis. Moreover, we observed the following drawbacks of clinical diagnosis: 1) Higher time in clinical diagnosis as it is done manually. 2) Diagnosis is subjective in nature 3) Accurate severity level of DED is not identified and 4) Medication may be prescribed for incorrect period on the basis of inaccurate severity level. Hence, we have proposed a deep learning based automated approach to diagnose DED considering Tear Film Breakup Time (TBUT) which is a standard diagnostic procedure. Our automated approach is to assist ophthalmologist and optometrist to bring objectivity in diagnosis, to increase diagnosis accuracy and to make diagnosis faster so that ophthalmologist can devote more time in counselling of patients.
To the best of our information, ours is the first attempt to automate TBUT based DED diagnosis. Discussion of this case study will include precise problem statement, steps involved in the solution, data set generated consulting ophthalmologists, experimental results and challenges faced & overcame to achieve this success. Finally, I will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.
Outline/Structure of the Case Study
- Dry eye disease and its complications in patients (7 mins)
- Issues in clinical screening of dry eye disease (3 mins)
- Problem statement (2 mins)
- Proposed deep learning based solution (8 mins)
- Discussion of dataset (5 mins)
- Experimental results and discussion (10 mins)
- Future research directions (5 mins)
- Q & A (5 mins)
- After attending this session, the attendees will ...
- understand how potential of deep learning can be utilized to diagnose unexplored or rarely focused diseases.
- learn how to overcome healthcare challenges using deep learning.
Data Scientists, Machine Learning/Deep Learning Practitioners, Researchers, Doctors, Students & Faculty Members from sectors such as Engineering and Technology as well as Medical.
Prerequisites for Attendees
- Familiarity with fundamentals of machine learning and deep learning.