- December 02, 2021
Currently, highly trained technicians can identify various eye diseases using optical coherence tomography (OCT), which typically requires a large benchtop system. This technique also demands that the patient set their head into a stationary head and chin rest, to ensure that they do not move and interfere with the eye scan. The scan itself involves shining a beam of light into the eye and then analyzing the reflected light to infer the presence of various diseases, such as age-related macular degeneration, glaucoma, and diabetic retinopathy.
These systems work well, but in the age of COVID many would prefer not to share a head and chin rest with every other patient. Moreover, the skilled technicians required to operate these scanners can be in short supply, which is part of the motivation behind this new system. “Not everywhere has a resource like the Duke Eye Center, where we have access to these highly trained and specialized technicians like ophthalmic photographers,” said Ryan McNabb, a researcher involved in the study. “But with our new tool, you wouldn’t need advanced training to use it. We’re optimistic that something like this could easily be used in places like optometrist offices, primary-care clinics, or even emergency departments. OCT is a useful diagnostic tool, and these kinds of advances help make it easier for wider communities to access it.”
The heart of the system is a robotic arm, which is guided by two 3D cameras that identify where the patient is located. The robotic arm then rapidly scans the patient’s eyes, taking less than a minute to scan both; however, it does not make any physical contact with the patient during the scan. “The robotic arm gives us the flexibility of handheld OCT scanners, but we don’t need to worry about any operator tremor,” said Mark Draelos, another researcher involved in the study. “If a person moves, the robot moves with it. As long as the scanner is aligned to within a centimeter of where it needs to be on your pupil, the scanner can get an image that is as good as a tabletop scanner.”
“While this is a solution for image collection issues, we think it will pair incredibly well with recent advances in machine learning for OCT image interpretation,” said McNabb. “We’re really bringing OCT to the patients rather than limiting these tools to specialized clinics, and I think it will make it much easier to help a wider population of people.”
REFERENCE: MedGadget; 16 AUG 2021; Conn Hastings