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Machine learning-based prediction of tear osmolarity for contact lens practice
Izabela K Garaszczuk1, Maria Romanos-Ibanez2, Alejandra Consejo2
1Wroclaw University of Science and Technology, Wroclaw, Poland.
Summary
Machine learning models can predict tear osmolarity in contact lens wearers, aiding in dry eye detection. Key factors include tear film break-up time and Meibomian gland assessment for better ocular health.
Area of Science:
- Ophthalmology
- Biomedical Engineering
- Data Science
Background:
- Elevated tear osmolarity is linked to contact lens-induced dry eye, a common reason for discontinuing lens wear.
- Accurate measurement of tear osmolarity is clinically significant but challenging.
Purpose of the Study:
- To utilize machine learning techniques for estimating tear osmolarity.
- To predict tear osmolarity using routine clinical parameters in contact lens wearers.
Main Methods:
- Employed machine learning, regression, and classification techniques on data from 175 participants.
- Collected clinical data including symptom questionnaires (Ocular Surface Disease Index, DEQ-5), tear meniscus height (TMH), non-invasive keratometric tear film break-up time (NIKBUT), ocular redness, and Meibomian gland assessment.
Main Results:
- Advanced regression models explained 32% of osmolarity variability, with NIKBUT, TMH, ocular redness, Meibomian gland coverage, and DEQ-5 as key predictors.
- Classification models achieved ~80% accuracy in distinguishing low, medium, and high osmolarity levels, using similar key parameters.
Conclusions:
- Machine learning shows potential for contact lens research and practice.
- Assessing Meibomian glands and NIKBUT is clinically useful for contact lens fitting and follow-up.
- ML models can optimize contact lens prescriptions and facilitate early dry eye detection.
