Automatic etiological classification of stroke thrombus digital photographs using a deep learning model

Autores de IIS La Fe

• Álvaro Lucero Garófano

  Autor

• Alicia Aliena Valero

  Autor

• Isabel Vielba Gómez

  Autor

• Irene María Escudero Martínez

  Autor

• Lluis Morales Caba

  Autor

• Fernando Aparici Robles

  Autor

• Diana Lucía Tarruella Hernández

  Autor

• Gerardo Fortea Cabo

  Autor

• José Ignacio Tembl Ferrairo

  Autor

• Juan Bautista Salom Sanvalero

  Autor

Participantes ajenos a IIS La Fe

• Manjón, JV

Grupos

• Unidad Mixta de Investigación Cerebrovascular

Abstract

Background Etiological classification of ischemic stroke is fundamental for secondary prevention, but frequently results in undetermined cause. We aimed to develop a Deep Learning (DL)-based model for automatic etiological classification of ischemic stroke using digital images of thrombi retrieved by mechanical thrombectomy.Methods Patients with large vessel occlusion stroke subjected to mechanical thrombectomy between April 2016 and January 2023 at La Fe University and Polytechnic Hospital in Valencia were included. Thrombus digital images were obtained and clinical characteristics, including TOAST etiological classification as reference standard, were retrieved. Statistical analysis was performed to compare clinical characteristics between atherothrombotic and cardioembolic strokes. A DL method was designed based on two deep neural networks for: (1) image segmentation and (2) image classification including clinical characteristics. The metrics used were DICE coefficient for the segmentation network, and accuracy, precision, sensitivity, specificity and area under the curve (AUC) for the predictions of the classification network.Results A total of 166 patients (mean age 69 [SD, 13], 67 female) were included. TOAST classification was: 31 atherothrombotic, 87 cardioembolic, and 48 cryptogenic. The segmentation network achieved an average DICE coefficient of 0.96 [SD, 0.13]. The optimal fused imaging and clinical classification network had a 0.968 accuracy [95% CI, 0.935-0.994], and AUC of 0.947 [95% CI, 0.870-1]. Cryptogenic thrombi were classified as cardioembolic (96%) or atherothrombotic (4%).Conclusion Two convolutional neural networks perform the automatic segmentation of thrombus images and, combined with selected clinical characteristics, their accurate and precise classification into atherothrombotic or cardioembolic etiology in patients with acute ischemic stroke.

Copyright © 2025 Lucero-Garófano, Aliena-Valero, Vielba-Gómez, Escudero-Martínez, Morales-Caba, Aparici-Robles, Tarruella Hernández, Fortea, Tembl, Salom and Manjón.

Keywords

• ischemic stroke; etiology; artificial intelligence; deep learning; segmentation; classification