Pathai, Inc.

Benjamin P Glass

Aditya Khosla

Andrew H Beck

Ilan N Wapinski

Harsha Vardhan Pokkalla

Hunter L Elliott

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 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

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Benjamin P Glass