Duke researchers develop automated device with potential to help diagnose and monitor common eye diseases
July 22, 2021
NEI
Female shown from overhead standing with her eye open receiving OCT scan.

The Duke-designed OCT has a robotic arm and an active-tracking scan head that automatically aligns itself with the patient’s pupil. It’s also contactless. Patients simply stand or sit in front of the scanner; there’s no need for a chinrest for stabilization because the system corrects for a patient’s subtle movement. Credit: Mark Draelos

A National Eye Institute-funded project at Duke University has yielded a fully automated optical coherence tomography (OCT) device that does not require a trained operator and promises to broaden access to retinal imaging technology.

OCT, which has become widely used by ophthalmologists over the past 25 years, uses light waves to image tissue layers and has become the gold standard for detecting and monitoring abnormalities of retinal tissue associated with diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy. Joseph A Izatt, Ph.D., professor of biomedical engineering and of ophthalmology at Duke, led the team.

A conventional OCT device is a large tabletop instrument that requires dedicated space and a trained operator. As such, its use is mostly limited to ophthalmology offices and large eye centers. By contrast, the availability of an automated OCT system could potentially enable primary care physicians to include structural eye exams in annual physicals or use of OCT in emergency care settings.

The Duke-designed OCT has a robotic arm and an active-tracking scan head that automatically aligns itself with the patient’s pupil. It’s also contactless. Patients simply stand or sit in front of the scanner; there’s no need for a chinrest for stabilization because the system corrects for a patient’s subtle movement.

“The capability exists to bring the OCT scanner to the patient, rather than the patient to the scanner, without sacrificing motion stabilization as with handheld scanners,” the researchers wrote July 12 in Nature Biomedical Engineering.

Images obtained via the Duke OCT system would require interpretation by an ophthalmologist.  However, current machine learning techniques for ophthalmic OCT analysis could potentially automate interpretation, said Mark Draelos, M.D., Ph.D., the study’s first author and a postdoctoral associate in Duke’s general surgery residency program.

Next steps include expanding the retinal field of view and comparing the system with standard-of-care conventional OCT systems.

The work was supported by NEI grants F30-EY027280, R01-EY029302 and U01-EY028079. NEI is part of the National Institutes of Health.

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Reference:

Draelos M, Ortiz P, Qian R, Viehland C, McNabb R, Hauser K, Kuo AN, Izatt JA. Contactless optical coherence tomography of the eyes of freestanding individuals with a robotic scanner. Nat Biomed Eng (2021). https://doi.org/10.1038/s41551-021-00753-6

 

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