Machine learning-based prediction of tear osmolarity for contact lens practice.

Journal: Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)

PMID: 38525850

Abstract

PURPOSE: This study addressed the utilisation of machine learning techniques to estimate tear osmolarity, a clinically significant yet challenging parameter to measure accurately. Elevated tear osmolarity has been observed in contact lens wearers and is associated with contact lens-induced dry eye, a common cause of discomfort leading to discontinuation of lens wear.

Authors

Izabela K Garaszczuk

Wroclaw University of Science and Technology, Wroclaw, Poland.
Maria Romanos-Ibanez

Aragon Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Alejandra Consejo

Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium.

Keywords

Adult Contact Lenses, Hydrophilic Dry Eye Syndromes Female Humans Machine Learning Male Middle Aged Osmolar Concentration Surveys and Questionnaires Tears Young Adult

External Resources

View on PubMed Access via DOI PubMed (38525850)

Machine learning-based prediction of tear osmolarity for contact lens practice.

Abstract

Authors

Keywords

External Resources

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