Information Entropy-Based Framework for Quantifying Curve Tortuosity in Meibomian Glands Uneven Atrophy

Area of Science:

• Medical Image Analysis
• Quantitative Morphology
• Ocular Surface Disease Diagnostics

Background:

• Precise quantification of curve tortuosity is vital for medical image analysis, aiding in disease diagnosis and pathological assessment.
• Meibomian gland (MG) atrophy uniformity is a key indicator in ocular surface disease, requiring accurate measurement.

Purpose of the Study:

• To introduce a novel framework for curve tortuosity quantification.
• To demonstrate its effectiveness in evaluating meibomian gland atrophy uniformity.
• To establish a generalizable tool for quantitative morphological evaluation in medical diagnostics.

Main Methods:

• Developed an information entropy-based framework for MG boundary curve tortuosity quantification.
• Integrated probability modeling, entropy theory, and domain transformation of curve data.
• Validated through numerical simulations and application to differentiate Demodex-negative and Demodex-positive patient groups.

Main Results:

• The framework successfully quantified spatial uniformity of MG atrophy.
• A significant difference in tortuosity-based uniformity was observed between Demodex-negative and Demodex-positive groups.
• Achieved an area under the curve of 0.8768, with 0.75 sensitivity and 0.93 specificity.

Conclusions:

• The proposed framework offers clinical utility for MG boundary curve tortuosity analysis.
• It serves as a potential generalizable tool for quantitative morphological evaluation in medical diagnostics.
• The method bridges morphometric research and clinical diagnostics for ocular surface disease using reference curves.