Paige.ai, Inc.

Peter Schueffler

Razik Yousfi

Thomas Fresneau

Alexander Tsema

Alexandre Kirszenberg

Leo John Grady

Thomas Fuchs

Jillian Sue

A method may process an electronic image corresponding to a medical sample associated with a patient. The method may include receiving a selection of one or more artificial intelligence (AI) algorithms, receiving one or more whole slide images of a medical sample associated with a patient, performing a task on the whole slide images, using the one or more selected AI algorithms, the whole slide images being stored in a first container, the whole slide images being originated from a first user, the task comprising determining a characteristic of the medical sample in the whole slide images, based on the characteristic of the whole slide image, generating metadata associated with the whole slide image, and storing the metadata in a second container.

Systems and methods for analysis of processing electronic images with flexible algorithmic processing

A method for processing an electronic image including receiving, by a viewer, the electronic image and a FOV (field of view), wherein the FOV includes at least one coordinate, at least one dimension, and a magnification factor, loading, by the viewer, a plurality of tiles within the FOV, determining, by the viewer, a state of the plurality of tiles in a cache, and in response to determining that the state of the plurality of tiles in the cache is a fully loaded state, rendering, by the viewer, the plurality of tiles to a display.

Systems and methods to process electronic images to provide improved visualization and rendering of histopathology slides

Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.

Systems and methods of automatically processing electronic images across regions

Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

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Munich, Germany

Peter Schueffler