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

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.

Methods: We trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes.

Evidence for the Misfolding of the A1 Domain within Multimeric von Willebrand Factor in Type 2 von Willebrand Disease.

Von Willebrand factor (VWF), an exceptionally large multimeric plasma glycoprotein, functions to initiate coagulation by agglutinating platelets in the blood stream to sites of vascular injury. This primary hemostatic function is perturbed in type 2 dysfunctional subtypes of von Willebrand disease (VWD) by mutations that alter the structure and function of the platelet GPIbα adhesive VWF A1 domains. The resulting amino acid substitutions cause local disorder and misfold the native structure of the isolated platelet GPIbα-adhesive A1 domain of VWF in both gain-of-function (type 2B) and loss-of-function (type 2M) phenotypes.