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Advancing Ulcerative Colitis Monitoring with Deep Learning Models

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43EB030441-01
Agency Tracking Number: R43EB030441
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NIBIB
Solicitation Number: PA19-272
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-01
Award End Date (Contract End Date): 2021-02-28
Small Business Information
990 Spring Garden Street, 5th Floor
Philadelphia, PA 19123-2606
United States
DUNS: 093014574
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Robert Cheetham
 (215) 701-7713
 cheetham@azavea.com
Business Contact
 ROBERT CHEETHAM
Phone: (215) 701-7713
Email: cheetham@azavea.com
Research Institution
N/A
Abstract

Project Summary/Abstract
The number of practicing pathologists around the world is expected to decrease by as much as 30% over the
next two decades, with some of the world’s poorest countries having a ratio of only one pathologist to many
hundreds of thousands of people. At the same time, the diagnostic caseload that requires their expertise in
clinical trials and hospital settings will continue to grow. The digitization of pathology data, coupled with the
use of machine learning techniques for analyzing and scoring the data, provides exciting opportunities to make
the field of pathology more efficient and scalable, even as the workforce continues to evolve. Deep learning in
particular provides the potential to enhance the interpretation of medical images by improving the detection of
image-based biomarkers for a broad range of diseases.
Image interpretation plays an important role in patient eligibility and endpoint determination during the
course of clinical trials. For patients with ulcerative colitis, the development of trained and reliable algorithms
that can help pathologists identify disease progression and response to treatment in a timely and effective
manner can provide benefit in two important ways. First, it will help to ensure that the most appropriate score
for histological disease severity is being assigned to each image using the Robarts Histopathology Index (RHI)
or similar grading scale. Second, it will support a triage process by which images known to contain non-
healthy tissues can be prioritized for earlier assessment.
Through a unique partnership between Azavea, a geospatial technology and machine learning firm, and
Robarts, a clinical trials organization, the proposed research will begin to address these needs by developing
deep learning algorithms for histopathology digital image analysis, testing them on machine-readable
annotations of medical imagery from previous clinical studies, and exposing them through a metadata-
searchable interface that will enable the images to be categorized and quickly accessed by pathologists and
others to support reader training and increase communication between multiple readers and sites. In so
doing, it will not only help streamline the evaluation of new ulcerative colitis treatments that rely heavily on the
image interpretation process, but also provide the foundation for the identification of additional components
present in other gastrointestinal disease indications in the future.Project Narrative
The proposed research will contribute critical new insights on the reliability, sensitivity, and practicality of
machine learning to support gastrointestinal disease detection and evaluation in a clinical trials setting. In
pathology, where manual interpretation of images using a microscope has remained relatively unchanged for
decades, machine learning provides particular potential to improve the speed and accuracy of diagnoses by
reducing the subjectivity that is often inherent in the process.

* Information listed above is at the time of submission. *

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