Evaluation of Saccadic Component Measure on Smooth Pursuit Tests

Area of Science:

• Ophthalmology
• Neuroscience
• Biomedical Engineering

Background:

• Smooth pursuit (SP) eye movement evaluation benefits from both qualitative and quantitative data.
• Current eye-tracking technology advancements necessitate exploring optimal classification methods for SP.
• This study addresses the need for improved consistency in clinical SP assessments.

Purpose of the Study:

• To assess inter-rater reliability among clinicians evaluating SP using standard categories.
• To determine if combining 'grossly normal' (GN) and 'mildly abnormal' (MA) into a 'subclinical' (SUBC) category improves agreement.
• To evaluate the sensitivity and specificity of the computerized percent saccade (PS) metric for SP classification.

Main Methods:

• Retrospective analysis of 70 participants' horizontal and vertical SP eye-tracking videos and data.
• Three clinicians rated SP performance using 4 categories (normal, GN, MA, AB) and then 3 categories (normal, SUBC, AB).
• Computerized percent saccade (PS) metric's sensitivity and specificity were calculated using generated cut-off values.

Main Results:

• Fair overall agreement was observed among clinicians using 4 SP categories.
• Combining GN and MA into a SUBC category led to a slight improvement in inter-rater agreement for both horizontal SP (HSP) and vertical SP (VSP).
• The PS metric demonstrated good sensitivity and specificity when thresholds were exceeded for two or more frequencies.

Conclusions:

• Combining 'grossly normal' and 'mildly abnormal' categories into a 'subclinical' classification enhances clinician agreement in SP evaluation.
• The computerized percent saccade metric is a valuable tool for objective SP classification.
• Integrating qualitative and quantitative data improves the comprehensive evaluation of smooth pursuit eye movements.