The Integration Of Artificial Intelligence In Retinal Imaging Devices

Artificial Intelligence (AI) has progressively been reshaped from cutting edge technology into practical applications with recent advances in deep learning technologies. Artificial Intelligence (AI) has undergone a period of explosive growth in many sectors recently, and healthcare is no exception to this.

AI will also have particular healthcare utility, and will significantly change the pathways of diagnosis and treatment for many, if not most, diseases. Irrespective of the specific technique, computer algorithms are to be used to identify relevant data information and to support clinical decision-making by using these technologies in medicine.

The application of AI technology in retinal imaging devices has evolved rapidly in many developed countries, at least in part as it improves human resources and skills and enhances the precision of medical treatment. As several countries that support advanced technology development fully support the coming AI era, they will start developing the necessary governance specifications by regulation, law, technology, and standards to completely optimize this developing technology field.

What are retinal imaging devices?

Retinal imaging devices help eye care professionals identify, assess, document, and treat retinal disorders. The advanced retinal imaging devices can view the retina by a single capture up to 0 to 200 degrees. In traditional devices, full-spectrum white light is used; however, advanced retinal imaging devices use low-powered laser light.

Retinal imaging takes a digital photograph to the back of the eye. It shows the retina (where light and objects hit), the optic disk (a location on the retina that houses the optic nerve, transmitting data to the brain), and the blood vessels. It helps to find out optometrist or ophthalmologist illnesses and to check the health of the eyes.

The different types of retinal imaging devices:Fundus Camera

Fundus photography has become widely accepted for DR screening as the availability of imaging platforms tends to increase. While binocular slit-lamp ophthalmoscopy has continued to be a standard against which other DR screening methods are judged, fundus photography is more cost-effective and requires no consultation with an ophthalmologist. With training and sufficient quality of the retinal image, graders can differentiate eyes with different DR levels of severity and recognize eyes with PDR that need an urgent referral for care.

Fluorescein Angiography

Fluorescein angiography (FA) is when the ophthalmologist takes photos of a retina using a special camera. These pictures help the ophthalmologist in getting a better look at the back of the eye to the blood vessels and other structures. FA is frequently recommended for finding and diagnosing eye diseases such as macular edema, macular degeneration, diabetic retinopathy, vein blockage inside the eye, termed BRVO or CRVO, macular pucker and ocular melanoma (a type of eye-affecting cancer). Furthermore, FA is used to track changes in eye disease over time and target areas of treatment.

OCT (Optical Coherence Tomography)

Optical Coherence Tomography (OCT) is considered one of the research tools that revolutionized ophthalmology and greatly helped the ophthalmologist in the diagnosis, treatment, and follow-up of many subsequent diseases in the segment. It is a non-invasive non-contact imaging method that offers a high-resolution cross-sectional image that approaches that of the histological sections of the head of the cornea, retina, choroid and optic nerve, its non-invasive nature and high-resolution images have made OCT particularly useful in detecting and managing retinal and choroidal diseases. Spectral-domain OCT utilizes near-infrared light to generate cross-sectional or three-dimensional (3D) retinal images.

How has artificial intelligence made its way into retinal imaging devices?Smartphone AI Effectively Detects Diabetic Retinopathy

Artificial Intelligence (AI) and Deep Learning System (DLS) can improve screening coverage in limited-resource environments. In DLS, neural networks read image labels with normal and abnormal results. It then begins to recognize patterns and groups of similar images of a particular diagnosis. The more the number of images are captured, the higher the precision and accuracy.

Advancements in retinal imaging have contributed to a paradigm shift in the treatment and management of retinal diseases. In the future, the use of new technologies such as AI and DLS for screening systems is likely to help detect and treat many blinding retinal disorders at an early stage. The extraordinary type of artificial intelligence (AI) called deep learning is also more widely used in research than in clinical practice, which is starting to give scientists a better way of processing images.

The workflow of deep learning

In both the technical and popular lexicons, AI is widely used to cover a range of learning, including but not restricted to machine learning, representational learning, deep learning, and natural language processing. Deep learning is making significant advancements in solving problems that have long resisted the AI community's best efforts. The discovery of intricate structures in high-dimensional data is very good and therefore relevant to multiple medical domains.

Deep learning applications, especially in retina images, range across classification, e.g. detection of diabetic retinopathy (DR) and diabetic macular edema (DME) in fundus photographs. It also comprises segmentation, e.g., segmentation of brain, lung, cell mitosis; and prediction, e.g., development and progression of myopia.

AI application in the clinic

Machine learning in ophthalmology has proven its great potential. Most of the existing studies on intelligent eye disease diagnosis concentrate on issues of dual classification while many patients suffer from numerous categorical retinal diseases in the clinical setting. Therefore, it is essential to have a model to concurrently detect and distinguish DR, AMD, glaucoma and other retinal disabilities. A newer generation of AI developed as a broad strategy will boost and enhance the applications of AI in the medical field.

AI plays a key role in the diagnosis and treatment of diseases, health management, drug research and development, precision medicine, etc. It can make a significant contribution to solving problems involving the uneven distribution of medical resources, reducing costs and improving the efficiency of treatments. Applying AI aims to compensate for the shortcomings of inadequate medical resources, promote the equity of medical services and advance hierarchical diagnosis and treatment construction. AI will also provide important support for the establishment of an integrated medical service system in the future. With the assistance of information-based systems, a qualified and effective integrated medical service system can be developed.

Industry players are implementing various strategies to help the industry grow

Sep-2018: Optos, a Nikon brand, launched its new ultra-widefield system for the European ophthalmic market, Monaco. This is the only ultra-widefield retinal imaging system with integrated Tomography of Optical Coherence (OCT). In less than half a second, the device produces a single-capture optomap image of 200°.

Feb-2019: Optomed made improvements to their camera Optomed Aurora. The additional features range from faster and smarter autofocus, analysis of image quality and four image sequences, new ways to select a patient list and integrated target led selection.

Aug-2019: Nidek launched Laser Ophthalmoscope Mirante scanning. It is an incredible platform for multimodal fundus imaging which combines OCT and SLO high definition with ultra-widefield imaging. It captures angiography (FA), high-quality color images, indocyanine green angiography (ICG), distinctive Retro mode images, FAF, OCT-Angiography, and OCT scans.

Oct-2019: Optos, a Nikon Company, introduced the first-of-its-kind Silverstone which incorporates ultra-wide retinal imaging with integrated; image-guided, swept OCT source. The device generates a 200° single capture optomap image with guided OCT, enabling specialized OCT imaging from the posterior pole to far periphery anywhere across the retina.

To conclude

While optical imaging can provide unprecedented picture resolution and speed, there are still clinical challenges in the field. One clinical challenge is the shortage of appropriate biomarkers. Current clinical biomarkers based on the imaging do not provide an adequate correlation between anatomy and function. Imaging has been an essential part of giving the retinal imaging devices industry their current understanding of the retinal and choroidal disease. With various advancements, including OCTA, OCT, adaptive optics, SLO, photoacoustic microscopy (PAM), fundus autofluorescence (FAF), and molecular imaging, the industry is currently at the verge of a revolution in retinal optical imaging.

Free Valuable Insights: Global Retinal Imaging Devices Market to reach a market size of $6.3 billion by 2025

Imaging modalities like these have begun to change their understanding of retinal disease's molecular pathogenesis, and are playing a progressive role in a patient's early diagnosis and management. Constant developments in imaging technology and progress in understanding retinal pathophysiology will continue to make optical imaging a critical technology in retinal diseases for many years to come.