Technical and clinical validation of a novel deep learning-based white matter hyperintensity segmentation tool.

ABSTRACT:

Introduction

White matter hyperintensities (WMH) on MRI are a hallmark of cerebral small vessel disease. Although numerous WMH segmentation tools exist, each presents relevant limitations that can impact their usability. This research aimed to develop, validate, and disseminate a novel WMH segmentation algorithm to address these limitations.

Methods

Using an intentionally heterogeneous dataset, we trained models based on the MD-GRU and nnU-Net deep learning algorithms. The new models were benchmarked in both technical and clinical validation against current state-of-the-art algorithms, utilizing datasets that were not included in the training data. For technical validation in patients, we assessed bias and precision against reference masks, scan-rescan repeatability and inte

SUBMITTER: Gesierich B 

PROVIDER: S-EPMC12448020 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature