There’s a growing interest to embed deep learning into medical devices for real-time image quality optimization. Requiring no operator expertise, embedded vision systems can now detect significant features for clinical diagnosis and prognosis with efficiency.
Ophthalmology has embraced the use of deep learning for imaging. Desktop retinal cameras are being replaced by portable fundus cameras (low-power microscope cameras), sometimes even available as smartphone add-ons. This advancement in technology is making ophthalmic imaging less expensive and accessible to more populations. Here are some ways embedded vision is improving ophthalmology diagnosis, prognosis, and treatment:
Applications for Embedded Vision in Ophthalmic Imaging
Detecting Dry Eye Disease
Dry eye disease is a common ophthalmic disorder. Embedded vision is being used to detect segmentation of dendritic cells. The density of corneal dendritic cells relates to both the symptoms and clinical signs of dry eye. It is a non-invasive and responsive biomarker to find the severity of corneal inflammation.
To evaluate the effect of inflammation on the cornea structure and function, images are taken using vivo confocal microscopy. Because manual classification of these images is time-consuming and subjective, embedded vision along with an automated convolution neural network is used to detect and quantify dendritic cells in the images. Deep learning then uses the images objectively to improve diagnostic and treatment accuracy.
Mapping the Vascular Tree in the Eye
Optical coherence tomography angiography (OCTA) is an imaging technique that produces high-resolution images of the vascular tree in the eye. It’s noninvasive, fast, and yields high-resolution 3D histological images. Embedded vision is used to create the 3D images.
OCTA shows a clear structure of blood flow in the eye. This helps physicians diagnose pathologies like age-related macular degeneration, vein occlusion, and glaucoma. OCTA compares sequential differences in the back-scattering of light reflected from a cross-section of the eye.
3D blood flow is visualized by detecting regions of differential phase variance in multiple 2D scans of individual slices of the retina. Physicians can quickly and accurately detect pathologies related to vascularization non-invasively with no side effects or clinical hazards.
Cataracts often affect the elderly, but not exclusively. A cataract occurs when the natural eye lens becomes clouded. Surgery is often required to replace the lens. Cataract surgery requires the removal of the patient’s current lens and replacing it with a new, synthetic lens.
The first step in the surgery is to place the eye within appropriate markers which use embedded vision to track the eye. Software remains aware of eye position and orientation. It helps the surgeon keep track of where cuts are made and what tool was used.
Embedded vision helps surgeons navigate through cataract surgery by means of an overlay on the microscope image. Software with sophisticated algorithms process the images and update the overlay in real time considering the movement of the spherical eye bulb.
You may also be interested in reading about Life Science Vision Applications for Medical Imaging at Vision Online.