Paige.AI. has filed a patent for systems and methods to verify slide and block quality for testing using machine learning models. The method involves analyzing digital images of tissue blocks to determine the presence of specific attributes and outputting a quality score based on the findings. GlobalData’s report on Paige.AI 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 Paige.AI, AI assisted radiology was a key innovation area identified from patents. Paige.AI's grant share as of January 2024 was 27%. Grant share is based on the ratio of number of grants to total number of patents.
Quality verification of tissue slides using machine learning model
A computer-implemented method for verifying slide and block quality for testing has been described in a filed patent. The method involves applying a machine learning model to determine the amount or percentage of tissue with a specific attribute from a digital image. A quality score is then outputted based on whether the determined amount of tissue with the attribute is below a predetermined value. The quality score is zero if the amount exceeds the predetermined value and is a linear combination of the attribute value and additional attribute values if it is below or equal to the predetermined value. The method also includes partitioning digital images into tiles, detecting and segmenting tissue regions, and determining the highest quality tissue block for testing.
Furthermore, the patent describes a system and a non-transitory computer-readable medium storing instructions for implementing the method using a machine learning model to verify slide and block quality for testing. The system includes at least one memory storing instructions and at least one processor executing the instructions to determine the amount of tissue with the attribute and output a quality score based on predetermined values. The method stored in the computer-readable medium also includes partitioning digital images into tiles, detecting and segmenting tissue regions, and removing background tiles. Overall, the patent outlines a comprehensive approach to utilizing machine learning models for assessing tissue quality in digital histopathology images, with a focus on determining the suitability of tissue blocks for testing based on specific attributes and quality scores.
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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 across the world’s largest industries.
