Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

Author:

Walger Lennart1ORCID,Adler Sophie2,Wagstyl Konrad3ORCID,Henschel Leonie4,David Bastian1ORCID,Borger Valeri5ORCID,Hattingen Elke6,Vatter Hartmut5,Elger Christian E.1,Baldeweg Torsten2ORCID,Rosenow Felix78,Urbach Horst9ORCID,Becker Albert10ORCID,Radbruch Alexander11,Surges Rainer1ORCID,Reuter Martin41213,Cendes Fernando14ORCID,Wang Zhong Irene15ORCID,Huppertz Hans‐Jürgen16,Rüber Theodor1ORCID

Affiliation:

1. Department of Epileptology University of Bonn Medical Center Bonn Germany

2. University College London Great Ormond Street Institute for Child Health London UK

3. Wellcome Centre for Human Neuroimaging London UK

4. German Center for Neurodegenerative Diseases Bonn Germany

5. Department of Neurosurgery University Hospital Bonn Bonn Germany

6. Department of Neuroradiology University Hospital and Goethe University Frankfurt Frankfurt am Main Germany

7. Epilepsy Center Frankfurt Rhine‐Main and Department of Neurology Goethe University and University Hospital Frankfurt Frankfurt am Main Germany

8. LOEWE Center for Personalized Translational Epilepsy Research Goethe University Frankfurt am Main Germany

9. Department of Neuroradiology Medical Center, University of Freiburg Freiburg Germany

10. Department of Neuropathology University Hospital Bonn Bonn Germany

11. Department of Neuroradiology University Hospital Bonn Bonn Germany

12. A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston Massachusetts USA

13. Department of Radiology Harvard Medical School Boston Massachusetts USA

14. Department of Neurology University of Campinas Campinas Brazil

15. Epilepsy Center Neurological Institute, Cleveland Clinic Cleveland Ohio USA

16. Swiss Epilepsy Center, Klinik Lengg Zürich Switzerland

Abstract

AbstractFocal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well‐annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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