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.
-
Jan 17, 2026, 05:05Dr Abdul Mannan: Iron Deficiency Can Hide Beta-Thalassaemia Trait
-
Jan 17, 2026, 05:01Fortis Memorial Research Institute, Gurugram Ranked Among the World’s Top 50 Smart Hospitals
-
Jan 17, 2026, 04:59Flora Peyvandi: Factor VIII in Vitro Bioequivalence of Mim8 Haemostatic Effect
-
Jan 17, 2026, 04:58Desmopressin in Bleeding Disorders: 5,000 Views in 6 Months – Why This Topic Matters
-
Jan 17, 2026, 04:58Sifat Jubaira Shares Chylous Blood Clinical Case: Milky Blood Instead of Red
-
Jan 17, 2026, 04:55Trends in Catheter-Directed Therapy and In-Hospital Outcomes in Acute PE
-
Jan 17, 2026, 04:54Antithrombin III Deficiency – Explained by Astha Thakkar
-
Jan 17, 2026, 04:53Antiphospholipid Syndrome: Not All Antibodies Are Created Equal
-
Jan 17, 2026, 04:53FDA Approval of Mitapivat: Redefining Thalassemia Management
