F. Hoffmann-La Roche. has filed a patent for a method and system to predict treatment outcomes for neovascular age-related macular degeneration (nAMD) using deep learning and symbolic models. The technology involves receiving retina imaging data, generating outputs, and predicting outcomes based on the input. GlobalData’s report on F. Hoffmann-La Roche gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on F. Hoffmann-La Roche, Cancer treatment biomarkers was a key innovation area identified from patents. F. Hoffmann-La Roche's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.

Predicting treatment outcome for neovascular age-related macular degeneration

Source: United States Patent and Trademark Office (USPTO). Credit: F. Hoffmann-La Roche Ltd

A patent application (Publication Number: US20240038370A1) discloses a method for predicting treatment outcomes for individuals undergoing treatment for neovascular age-related macular degeneration (nAMD). The method involves utilizing three-dimensional imaging data of the retina, generating outputs through deep learning systems and symbolic models, and incorporating baseline data such as demographic information, visual acuity measurements, and treatment arms to predict outcomes like visual acuity changes and central subfield thickness reductions. The treatment outcomes are forecasted at specific time points post-baseline, ranging from three to thirty months, and may involve therapies targeting vascular endothelial growth factors like monoclonal antibodies and angiopoietin 2 inhibitors, including faricimab.

Furthermore, the patent application extends to a system designed to manage anti-vascular endothelial growth factor (anti-VEGF) treatments for individuals diagnosed with nAMD. This system includes a processor and memory containing machine-readable code to receive three-dimensional imaging data, generate outputs using deep learning systems and symbolic models, and predict treatment outcomes based on the input data. The system integrates baseline information like demographic data and visual acuity measurements to forecast outcomes related to visual acuity and central subfield thickness changes. Additionally, the system may be utilized for treatments involving faricimab, showcasing its potential application in personalized medicine for nAMD patients.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies