PURPOSE: To evaluate the ability of deep learning (DL) models to detect obstructive meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images.
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
BACKGROUND: Artificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction (MGD...
Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Co...
Optometry and vision science : official publication of the American Academy of Optometry
39792877
SIGNIFICANCE: Optimal meibography utilization and interpretation are hindered due to poor lid presentation, blurry images, or image artifacts and the challenges of applying clinical grading scales. These results, using the largest image dataset analy...