Advancing Diagnostics with Semi-Automatic Tear Meniscus Central Area Measurement for Aqueous Deficient Dry Eye...

Area of Science:

• Ophthalmology
• Optometry
• Dry Eye Disease Research

Background:

• Dry eye disease (DED) is a prevalent condition with two main subtypes: Aqueous Deficient Dry Eye (ADDE) and Non-Aqueous Deficient Dry Eye (Non-ADDE).
• Accurate differentiation between ADDE and Non-ADDE is crucial for effective treatment strategies.
• Current diagnostic methods may require further refinement for precise classification.

Purpose of the Study:

• To clinically validate a semi-automatic measurement of Tear Meniscus Central Area (TMCA).
• To assess the capability of TMCA in differentiating between ADDE and Non-ADDE patients.
• To establish the diagnostic performance of TMCA as a potential clinical tool.

Main Methods:

• 120 participants underwent a battery of dry eye diagnostic tests, including TFOS DEWS II criteria.
• Tear meniscus videos were captured using Tearscope and fluorescein imaging.
• Semi-automatic TMCA was measured using NIH ImageJ software from fluorescein images.

Main Results:

• Receiver Operating Characteristics (ROC) analysis demonstrated significant diagnostic capability for TMCA.
• An optimal cut-off value of 54.62 mm² for TMCA showed diagnostic potential (AUC = 0.714 ± 0.051, p < 0.001).
• TMCA achieved 71.7% specificity and 68.9% sensitivity in differentiating ADDE from Non-ADDE.

Conclusions:

• Semi-automatic TMCA evaluation presents valuable preliminary results for distinguishing ADDE from Non-ADDE.
• TMCA shows potential as a non-invasive, objective diagnostic tool in clinical practice.
• Further validation may enhance its role in dry eye disease management.