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Ocular myasthenia gravis (OMG) is a challenging condition to diagnose, with Cogan's lid twitch (CLT) serving as a key clinical sign. Early and accurate diagnosis of OMG is crucial for timely intervention and improved patient outcomes. However, current diagnostic methods for CLT rely primarily on visual assessment by clinicians, which is inherently subjective and prone to interobserver variability, highlighting the need for more objective diagnostic tools. This pilot study was the first to investigate the potential of video-based eyelid tracking for simplified diagnosis of Cogan's lid twitch (CLT), a key sign of ocular myasthenia gravis (OMG). The importance of this research lies in its potential to enhance diagnostic accuracy, enable earlier treatment initiation, and improve the overall management of OMG patients. We analyzed pixel value changes and eyelid position in a video recording of CLT, employing preprocessing techniques to stabilize footage and reduce lighting artifacts. Eyelid tracking was performed using contour detection and polynomial fitting. Our results showed detectable variations in eyelid position during CLT occurrences, though with inconsistent accuracy. Challenges included distinguishing subtle CLT movements from normal blinks and addressing issues like double eyelids. While further refinement is needed, this research suggests the potential of video-based tracking as a non-invasive tool for OMG diagnosis, offering significant clinical implications.Clinical Relevance- This study suggests the potential for simplified diagnosis of Cogan's lid twitch through video-based tracking, indicating significant clinical implications.