Skip to main content

ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1518477
This article is part of the Research Topic AI's Transformative Role in Neuro-Intervention: Enhancing Diagnosis and Treatment Strategies View all 3 articles

Evaluation of Siemens Healthineers' StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke

Provisionally accepted
Lynnet-Samuel Joyce Teichmann Lynnet-Samuel Joyce Teichmann 1*Ahmed A Khalil Ahmed A Khalil 1Kersten Villringer Kersten Villringer 1Jochen Fiebach Jochen Fiebach 1Eli Gibson Eli Gibson 2Stefan Huwer Stefan Huwer 2Ivana Galinovic Ivana Galinovic 1
  • 1 Center for Stroke Research Berlin, Charité University Medicine Berlin, Berlin, Berlin, Germany
  • 2 Siemens Healthcare, Erlangen, Bavaria, Germany

The final, formatted version of the article will be published soon.

    To evaluate the Siemens Healthineers StrokeSegApp's performance in automatically segmenting diffusion and perfusion lesions in patients with acute ischemic stroke, and to assess its clinical utility in guiding mechanical thrombectomy decisions.This retrospective study used MRI data of acute ischemic stroke patients from the prospective observational single-center 1000Plus study, acquired between September 2008 and June 2013 (clinicaltrials.org; NCT00715533) and manually segmented by radiologists as the ground truth. The performance of the StrokeSegApp was compared against this ground truth using Dice Similarity Coefficient (DSC) and Bland-Altman plots. The study also evaluated the app's ability to recommend mechanical thrombectomy based on DEFUSE 2 and 3 trial criteria.The StrokeSegApp demonstrated a mean DSC of 0.60 (95% CI: 0.57 -0.63; n=241) for diffusion deficit segmentation and 0.80 (95% CI: 0.76 -0.85; n=56) for perfusion deficit segmentation. The mean volume deviation was 0.49ml for diffusion lesions and -7.69ml for perfusion lesions. Out of 56 subjects meeting DEFUSE 2/3 criteria in the cohort it correctly identified mechanical thrombectomy candidates with a sensitivity of 82.1% (95% CI: 63.1% -93.9%) and specificity of 96.4% (95% CI: 81.7% -99.9%).The Siemens Healthineers StrokeSegApp provides accurate automated segmentation of ischemic stroke lesions, comparable to human experts as well as similar commercial software, and shows potential as a reliable tool in clinical decision-making for stroke treatment.

    Keywords: Acute ischemic stroke, Automated lesion segmentation, MRI analysis software, diffusionweighted imaging, perfusion-weighted imaging, Siemens Healthineers StrokeSegApp

    Received: 28 Oct 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Teichmann, Khalil, Villringer, Fiebach, Gibson, Huwer and Galinovic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lynnet-Samuel Joyce Teichmann, Center for Stroke Research Berlin, Charité University Medicine Berlin, Berlin, 10117, Berlin, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.