Glaucoma is a disease often associated with vision loss. Unfortunately, this is true when diagnosed in its later stages, with visible signs of vision loss. There are 80 million people worldwide with glaucoma, and this number is expected to increase to over 111 million by 2040. While we cannot currently cure glaucoma, blindness or significant vision loss can be prevented if we can detect the earliest signs of glaucomatous changes in the eye. These signs may be undetectable to the human eye but not to a well-trained robotic eye.
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Machine Algorithms Against Glaucoma
Traditional glaucoma diagnosis relies on observable changes during routine examinations and monitoring changes over time. This workflow is well enough for some eye pathologies, where signs are visible early on and patients experience vision changes early. However, this is not the case for glaucoma. If a patient arrives at a clinic with visual impairments due to glaucoma, it is usually too late to preserve their vision.
The difference lies in the fact that many eye conditions affect the eyeball itself, while glaucoma affects the optic nerve connecting the eye to the brain. This leads to optic nerve atrophy, a neurodegenerative process that causes neurons to die.
Currently, the most common non-invasive method for detecting these changes is an Optical Coherence Tomography scan. Glaucomatous changes appear as a progressive degeneration of retinal ganglion cells and their axons. The macular ganglion cell complex, which includes the retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer, is often the focus of analysis, as these layers are particularly vulnerable to glaucomatous damage. These changes on OCT scans can be too subtle for even a trained doctor’s eye to detect. This is where artificial intelligence comes in.
AI can be trained to identify any deviation from the norm on the scan and can calculate the difference in the thinning of the ganglion cell complex to provide a comprehensive picture of the disease progression.
AI offers crucial insights into glaucoma detection. For example, one FDA-cleared platform uses AI to analyze OCT scans, focusing on the ganglion cell layer thickness and identifying any thinning or asymmetry. The glaucoma risk assessment of a patient is aided by these parameters. This is a crucial additional tool to have, especially when doing preventive exams or in addition to other tests, in order to identify any early warning indicators. Even though there is always variation in the medical sector and each patient is unique, artificial intelligence (AI) presents a promising path for earlier and more precise glaucoma diagnosis.
The Future of Glaucoma Treatment
The main challenge in glaucoma treatment is that we still don’t fully understand all the underlying processes. As we uncover more about the etiology of glaucoma, we can develop more novel treatment modalities. Continued research into the disease process of glaucoma itself will pave the way for newer surgical technologies and other disease pathway modulations.
The integration of AI into diagnosis, coupled with advances in our understanding of glaucoma, offers a hopeful future for earlier detection and more effective treatment of this vision-threatening disease. AI-powered OCT test for glaucoma is the future of accurate diagnostics.