Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Deployment and access to state-of-the-art diagnostic technologies remains a fundamental challenge in providing equitable global cancer care to low-resource settings. The expansion of digital pathology in recent years and its interface with computational biomarkers provides an opportunity to democratize access to personalized medicine. Here we describe a low-cost platform for digital side capture and computational analysis composed of open-source components. The platform provides low-cost ($200) digital image capture from glass slides and is capable of real-time computational image analysis using an open-source deep learning (DL) algorithm and Raspberry Pi ($35) computer. We validate the performance of deep learning models’ performance using images captured from the open-source workstation and show similar model performance when compared against significantly more expensive standard institutional hardware.

Authors 12

1. Divya Choudhury

   University of Chicago

   Affiliation as printed

   Pritzker School of Medicine, University of Chicago, Chicago, IL

2. James M. Dolezal

   University of Chicago Medical Center

   Affiliation as printed

   Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA

3. Emma Dyer

   University of Chicago Medical Center

   Affiliation as printed

   Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA

4. Sara E. Kochanny

   University of Chicago Medical Center

   Affiliation as printed

   Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA

5. Siddi Ramesh

   University of Chicago

   Affiliation as printed

   Pritzker School of Medicine, University of Chicago, Chicago, IL

6. Frederick Matthew Howard

   University of Chicago Medical Center

   Affiliation as printed

   Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA

7. Jayson R. Margalus

   DePaul University

   Affiliation as printed

   DePaul University, Idea Realization Lab, Chicago, IL, USA

8. Amelia R. Schroeder

   DePaul University

   Affiliation as printed

   DePaul University, Idea Realization Lab, Chicago, IL, USA

9. Jefree J. Schulte

   University of Wisconsin–Madison

   Affiliation as printed

   Department of Pathology and Laboratory Medicine, University of Wisconsin School of Medicine and Public Health

10. Marina Chiara Garassino

    University of Chicago Medical Center

    Affiliation as printed

    Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA

11. Jakob Nikolas Kather Aachen Department of Medicine III

    German Cancer Research Center · Universitätsklinikum Aachen · Deutsches Konsortium für Translationale Krebsforschung · RWTH Aachen University

    Affiliation as printed

    Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

    German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

12. Alexander T. Pearson corresponding

    University of Chicago Medical Center

    Affiliation as printed

    Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA