On the clinical acceptance of black‐box systems for EEG seizure prediction

Abstract: Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state-of-the-art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor-Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human- comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch
Notes
Epilepsia open. - 7, 2 (2022) , 247-259, ISSN: 2470-9239

Classification
Medizin, Gesundheit

Event
Veröffentlichung
(where)
Freiburg
(who)
Universität
(when)
2024
Creator
Pinto, Mauro F.
Leal, Adriana
Lopes, Fabio
Pais, José
Dourado, Antonio
Sales, Francisco
Martins, Pedro
Teixeira, César Alexandre

DOI
10.1002/epi4.12597
URN
urn:nbn:de:bsz:25-freidok-2448474
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
25.03.2025, 1:55 PM CET

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Associated

  • Pinto, Mauro F.
  • Leal, Adriana
  • Lopes, Fabio
  • Pais, José
  • Dourado, Antonio
  • Sales, Francisco
  • Martins, Pedro
  • Teixeira, César Alexandre
  • Universität

Time of origin

  • 2024

Other Objects (12)