An Intelligent and Handheld Device for Early Identification of Meibomian Gland Irregularities

Abstract

Meibomian gland dysfunction (MGD) is a significant cause of evaporative dry eye disease, occurring when the meibomian glands (MGs) in the eyelids produce abnormal lipid amounts. MG morphological features are crucial indicators of MG function and dry eye symptoms. However, the relationship between MG morphological irregularities and MGD remains unclear. To address this, we develop an integrated deep-learning-enabled monitoring system within a portable meibography device, enabling early identification and quantification of irregularly-shaped MGs. Our approach comprises two key technical components. First, a customized model is fine-tuned to classify MG irregularities into four types: overlapping, shortening, thickening, and tortuosity. We then quantitatively analyze MG irregularity ratios among four meiboscore groups of varying MG atrophy degrees and examine their connection to Ocular Surface Disease Index (OSDI) indexes from a subjective symptom perspective. From meiboscore 0 to 3, the overlapping MG ratio decreases by 17 %, and the shortening MG ratio increases by 12 %. Furthermore, we’ve built a handheld device equipped with infrared (IR) LED arrays and a USB camera to facilitate long-term and dynamic assessment. This meibography technology is compatible with common operating systems and can be integrated into a smartphone. The high-resolution images captured by this device can be used to assess various types of irregularities. This intelligent portable system offers an automatic and efficient quantitative evaluation of MG morphological irregularities, enabling home inspection and reducing costs. It has the potential to be applied in diagnosing and monitoring MG conditions, facilitating the management of MGD.