Aging global populations are increasing the prevalence of chronic diseases, particularly ophthalmic and cardiovascular conditions, fueling demand for early and accurate diagnosis. Simultaneously, advancements in digital health and AI-driven diagnostics are pushing for higher image quality and data reliability. This technology aligns perfectly with these trends by providing a foundational improvement in OCT imaging, enabling more precise diagnostics and supporting the shift towards preventative and personalized medicine across healthcare systems worldwide.
Significantly Enhances Diagnostic Accuracy: Corrects biological motion blur and aberrations, potentially improving diagnostic accuracy by up to 20% for subtle lesions previously difficult to visualize with conventional OCT.
Optimizes Examination Efficiency: Reduces the need for re-examinations and obtains high-quality images in a single scan, potentially easing the burden on medical professionals and increasing patient throughput by 1.5 times.
Strong IP Protection and Stability: Features 11 claims and involvement from multiple prominent agents, ensuring a robust and precisely defined scope of protection. Patentability was confirmed against 8 prior art documents, providing a stable and defensible right for commercial use.
This patent protects an information processing apparatus, method, and computer program across 11 claims. Its scope is robust and was thoroughly examined, having overcome a rejection notice with precise arguments, indicating a stable and defensible right.
This patent focuses on image correction within OCT. Licensees could build additional IP in areas like AI-driven automated diagnostic interpretation, multi-modal imaging fusion, or novel OCT hardware designs beyond the current software-centric approach.
Assuming a 10% reduction in re-examination rates with this technology. For a medical institution performing 10,000 OCT examinations annually, with an average cost of $100/case (AI est.) (including personnel and equipment operation), the annual savings are estimated at 10,000 cases × 10% × $100/case = $100,000 (AI est.). This effect could scale to ~$1M/year (AI est.) when deployed across large medical systems or diagnostic centers.
X: Diagnostic Accuracy
Y: Examination Efficiency