Eyes on the Prize: Spotlight on AI-Driven Innovations
The advancement of artificial intelligence (AI) is driving innovation in ophthalmic research. While there are still challenges to be addressed, there is marked progress in applying AI to improve many aspects of ophthalmic research and trials. In the final part of our “Eyes on the Prize” blog series, we will spotlight two exciting AI-driven innovations and dive deeper into the future of AI in ophthalmology.
AI-Driven OCT Analysis
Optical coherence tomography (OCT) is a fast, low-cost and repeatable imaging technique that generates high-resolution, three-dimensional images of the retina from which anatomical biomarkers may be quantified. AI-driven OCT analysis is on the rise as the ophthalmology sector seeks more advanced analytics and new solutions to overcoming bottlenecks and inefficiencies slowing down trials and research. The limited functionality of existing OCT tools has constrained analysis to only two or three layers of retinal segmentation, despite the clinical importance of the inner retinal layers in disease detection and prognosis. Using AI-enhanced analysis enables more advanced segmentation while reducing the time-consuming process of image grading. In addition, using AI can make read times 8-10x faster as it offers automated precision measurements and intuitive editing.
In addition, one of the main barriers to efficient OCT analysis has been the lack of interoperability between vendor-specific, non-DICOM formats. AI-enhanced analysis uses a consistent approach when grading image data, ensuring precise and reproducible results across different devices.
Leveraging AI-backed OCT analysis and segmentation produces higher accuracy, increases throughput, and minimizes the risk of human errors. This is especially important as the number and size of ophthalmic images continue to grow, and the risk of error increases with every human touchpoint. AI’s ability to handle large quantities of data lowers the need for manpower and further reduces ophthalmic clinical trial costs – which are the second highest in the medical industry. Speeding up the analysis and grading of image data raises the chances for trials to be successfully completed on time and within budget.
Orion, by Voxeleron, provides AI-enhanced OCT and ophthalmic image analysis and is fully automated and entirely platform-independent. The first of its kind to be truly vendor-neutral, Orion processes data from all major OCT scanners and ophthalmic cameras in any of their formats. Orion leverages AI to enable researchers to perform 8-layer segmentation and 3D visualization. By addressing existing technology gaps, Orion can help reduce clinical trial costs while providing the needed biomarkers for early detection and prognosis of ocular and neurological diseases.
Clinical Trial Management
An additional application of AI that is just now coming into focus is within clinical trial management platforms and tools. As mentioned in our first blog in this series, 40% of clinical trials in ophthalmology are discontinued due to poor recruitment. Utilizing AI, clinical trial managers can more accurately analyze participant eligibility, to ensure they would be a good fit for the trial and to not miss a crucial detail that could disqualify them. AI-based prognostic models can also be used to boost the trial population with fast-progressors – those who could more quickly give definitive results — effectively lowering both the number of subjects required and the length of the trial.
In ophthalmic clinical trials the number of images and data collected and analyzed grows over the trial period. This creates a challenge for the trial managers not only for data storage but also data organization and retrieval. AI is helping here as well by prescribing efficient indexing and streamlined workflows to ensure the data is accessible whenever and wherever a researcher needs it, saving valuable time and effort.
Voxeleron recently introduced its cloud-based clinical trial management platform, iNebula, which applies AI and machine learning to streamline workflows. By eliminating costly bottlenecks that plague lengthy ophthalmic clinical trials, managers can keep multiple trials running efficiently. Given the average time for a new drug to reach the market is around 7.5 years, this application of AI has the potential to significantly accelerate ophthalmic research and resulting treatments and outcomes.
Innovations on the Horizon
As AI continues to evolve there is no doubt of the value it is already bringing to the ophthalmology sector. More AI-powered innovations are emerging as new software and platforms are being introduced. In the ophthalmology sector, advancements are being made in telehealth and virtual treatments of conditions like AMD, diabetic retinopathy, and glaucoma, but more work is needed. For example, there is a critical need for more reliable and higher-quality imaging to be available for telehealth patients. As AI enables ubiquitous image data standardization, an exciting opportunity arises to bridge any gap between image quality from clinical and non-clinical environments. This would allow for comprehensive data standardization, and accurate and longitudinal image comparison, benefiting trials, new therapeutics, diagnosis, and ultimately clinical management for a broad range of patients, no matter their location.
If you are looking to learn more about innovative AI-backed platforms that can provide efficient clinical trial management and facilitate advanced OCT analysis, we would love to give you more insight into our Orion and iNebula platforms. Get in touch with us today or, visit voxeleron.com to learn more and request a demo.