Automated Von Willebrand Factor Multimer Image Analysis for Improved Diagnosis and Classification of Von Willebrand Disease.

Journal: International journal of laboratory hematology
Published Date: Mar 2, 2025

Abstract

INTRODUCTION: Von Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter-rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.

Authors

  • Karthik Anand
    Mayo High School, Rochester, Minnesota, USA.
  • Vincent Olteanu
    Mayo High School, Rochester, Minnesota, USA.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Katelynn Nelton
    Special Coagulation Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
  • Erin Aakre
    Special Coagulation Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
  • Juliana Perez Botero
    Special Coagulation Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
  • Rajiv Pruthi
    Special Coagulation Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
  • Dong Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Jansen N Seheult
    The Department of Pathology, University of Pittsburgh, and Vitalant Specialty Labs, Pittsburgh, Pennsylvania (Seheult).

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