Automated VWF Multimer Analysis Using Deep Learning for Improved Diagnosis and Classification of vWD
Dong Chen, Vice Chair of Practice in Pathology at Mayo Clinic, shared an articlle on LinkedIn:
“Automated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.”
Title: Automated Von Willebrand Factor Multimer Image Analysis for Improved Diagnosis and Classification of Von Willebrand Disease (vWD)
Authors: Karthik Anand, Vincent Olteanu, Chi Zhang, Katelynn Nelton, Erin Aakre, Juliana Perez Botero, Rajiv Pruthi, Dong Chen, Jansen N. Seheult
Read the full article here.
Explore latest hematology articles in Hemostasis Today.
-
Jun 8, 2026, 13:502025 EHA Annual Report Is Now Online
-
Jun 8, 2026, 13:49Lovish Garg: Advances in Endovascular Treatment for Deep Vein Thrombosis
-
Jun 8, 2026, 13:39Tasbeeh Ahmed: Contemporary Treatment Strategies for Chronic Venous Disease
-
Jun 8, 2026, 13:35Wolfgang Miesbach: Translating Gene Therapy into Haemophilia Care at EHA 2026
-
Jun 8, 2026, 13:27Emile Hung: Why Nutrition Should Be Central to Vascular Disease Prevention
-
Jun 8, 2026, 12:21William Aird: Developing Patient-Facing Educational Material for The Blood Project
-
Jun 8, 2026, 12:02Glanzmann Thrombasthenia – A Rare but Important Platelet Disorder – Iranian Comprehensive Hemophilia Care Center
-
Jun 8, 2026, 06:30Caryn Blumenfeld: Team Zakophilia and Ratner’s Runners Even Though It’s A Walk!
-
Jun 8, 2026, 06:25Cécile Jefford: The NEHA Community and the Power of Human Connection