Paige.AI has patented a method for processing digital images of human or animal tissue to predict quality and disease designations using machine learning models. The system compares external designations to disease predictions, providing a comprehensive quality control and assurance process for pathology analysis. 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 February 2024 was 26%. Grant share is based on the ratio of number of grants to total number of patents.
Quality control and assurance for digital pathology images
A recently granted patent (Publication Number: US11928820B2) outlines a computer-implemented method for processing electronic images related to pathology categories. The method involves receiving a digital image of human or animal tissue, determining quality control (QC) and quality assurance (QA) machine learning models to predict quality and disease designations, respectively. The QC model assesses artifacts in the image, providing an approval or rejection designation based on the presence of errors or imperfections. The QA model predicts disease properties based on biomarkers, comparing them to external designations such as cancer detection or grade. The system also generates reports on artifact types, clinical impact, and potential patterns, enhancing image quality assessment and disease diagnosis accuracy.
Furthermore, the patent details a system with memory storing instructions and a processor executing operations for image processing. The system follows a similar process of utilizing QC and QA machine learning models to evaluate image quality and predict disease designations based on biomarkers. It also includes features like generating reports on artifact types, clinical impact, and potential correlations, as well as automatically generating notifications based on quality characteristics. The system aims to streamline the assessment of electronic medical images, ensuring accurate diagnosis and treatment decisions by comparing disease designations to external properties and rejecting deviations beyond predetermined thresholds. Overall, the patent highlights a comprehensive approach to image processing in pathology categories, leveraging machine learning models to enhance quality control and disease diagnosis in medical imaging.
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