Artificial intelligence is doing fascinating things for health care. At best, doctors can be hopeful that partnering with these robo-techs will help to provide the most comprehensive picture of patients’ health with minimal invasiveness. For instance, at a recent Vision Expo meeting in Las Vegas, manufacturers boasted that deep learning software applied to diagnostic imaging devices, such as OCT and fundus cameras, can figure out a patient’s sex based from the retina image alone—and researchers don’t even know why.¹ Now, new research is showing that the same type of technology is able to diagnose meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images alone.²

“Our network combining deep learning and in vivo laser confocal microscopy learned to differentiate between images of healthy meibomian glands and images of obstructive MGD with a high level of accuracy that may allow for automatic obstructive MGD diagnoses in patients in the future,” the study says.²

Investigators relied on 137 images of obstructive MGD and 84 images of normal meibomian glands to “train” the deep learning software. Researchers noted a 94.2% sensitivity and an 82.1% specificity.²

The authors mentioned that they employed the Heidelberg Retina Tomograph II-Rostock Cornea Module (HRT II-RCM, Heidelberg) with a diode laser without everting the eyelid and obtained excellent results.²

Though real-world application of such technology is likely a ways away, the foundational work is now being done and increasingly proving its aptitude for myriad diagnostic responsibilities.