Pathai, Inc.

Andrew H Beck

Aditya Khosla

Ilan N Wapinski

Hunter L Elliott

Benjamin P Glass

Harsha Vardhan Pokkalla

Jennifer K Kerner

Sai Chowdary Gullapally

Ramprakash Srinivasan

Dayong Wang

Kenneth Knute Leidal

Maryam Pouryahya

Jason Ku Wang

Oscar M Carrasco-zevallos

Dinkar Juyal

Michael C Montalto

Zahil Shanis

Murray Resnick

Quang Huy Le

Aryan Pedawi

Charles Biddle-Snead

Katy Wack

Amaro N Taylor-Weiner

In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.

Systems and methods for assessing liver pathology

In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Systems and methods for training a model to predict survival time for a patient

In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. A pathology image is accessed. Frames are generated using the pathology image. Each frame in the set includes a distinct portion of the pathology image. Reference annotations are received from one or more users. The reference annotations describe at least one of a plurality of tissue or cellular characteristic categories for one or more frames in the set. Each frame in the set is processed using the trained model to generate model predictions. The model predictions describe at least one of the tissue or cellular characteristic categories for the processed frame. Performance of the trained model is validated based on determining a degree of association between the reference annotations and the model predictions for each frame and/or across all frames in the set of frames.

Systems and methods for frame-based validation

In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.

Systems and methods for training a statistical model to predict tissue characteristics for a pathology image

In some aspects, the described systems and methods provide for a method for predicting tissue characteristics for a pathology image. A statistical model trained on multiple annotated pathology images is used. Each of the training pathology images includes an annotation describing tissue characteristics for one or more portions of the image. The method includes accessing a pathology image for predicting tissue characteristics. A trained statistical model is retrieved from a storage device. A set of patches is defined from the pathology image. Each of the patches in the set includes a subset of pixels from the corresponding pathology image. The set of patches is processed using the trained statistical model to predict respective annotations for each patch in the set. The predicted annotations are stored on the storage device.

Systems and methods for predicting tissue characteristics for a pathology image using a statistical model

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Brookline, MA, United States of America

Andrew H Beck