Paige.AI has filed a patent for a computer-implemented method that processes electronic medical images. The method involves receiving digital medical images of pathology specimens associated with a patient, as well as search criteria. Based on the search criteria, one or more machine learning systems are determined and outputted to the user. The machine learning systems are then applied to the received medical images, and the analyzed images are displayed. The user can select a specific machine learning system, which will be outputted. 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 September 2023 was 27%. Grant share is based on the ratio of number of grants to total number of patents.
Computer-implemented method for processing electronic medical images
A recently filed patent (Publication Number: US20230290111A1) describes a computer-implemented method for processing electronic medical images. The method involves receiving digital medical images of pathology specimens associated with a patient, as well as search criteria. Based on the search criteria, one or more machine learning systems are determined and outputted to the user. The machine learning systems are then applied to the received medical images, and the analyzed images are displayed to the user. The user can select a specific machine learning system, which is then outputted.
The patent also describes the option to insert the digital medical images into the selected machine learning system, applying it to generate processed medical images, which are then outputted. The display of the images can include various visual representations such as heat maps, level of confidence maps, and color gradient maps of the search criteria. Additionally, a heat map overlay can be used to indicate the confidence value, representing the similarity of results between different machine learning systems.
The method further includes suggesting a particular machine learning system based on the search criteria. Additionally, a first machine learning system can be applied to the received medical images before outputting the machine learning systems to the user. This first machine learning system applies an initial filter to determine the area of tissues displayed in the medical images.
The search criteria can include factors such as training size, validation size, European CE Mark approval, U.S. Food and Drug Administration (FDA) approval, inputs, outputs, or function. It can also be based on a medical diagnosis.
The patent also describes a system for processing electronic digital medical images, which includes memory storing instructions and a processor executing the instructions to perform the operations described above.
In summary, this patent presents a computer-implemented method and system for processing electronic medical images using machine learning systems. The method allows for the analysis and display of medical images based on user-defined search criteria, with the option to select and apply specific machine learning systems. The system includes various visual representations and features to enhance the analysis and interpretation of the medical images.
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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.
