Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review
| Autor: | Jules C. Beal, Debopam Samanta, Zachary M. Grinspan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Freedom Drug Resistant Epilepsy medicine.medical_specialty Article Epilepsy Seizures Humans Medicine Epilepsy surgery Medical physics Child Retrospective Studies Estimation business.industry Statistical learning Electroencephalography Cognition Seizure freedom medicine.disease Identification (information) Treatment Outcome Epilepsy in children Pediatrics Perinatology and Child Health Neurology (clinical) business |
| Zdroj: | Semin Pediatr Neurol |
| ISSN: | 1071-9091 |
| DOI: | 10.1016/j.spen.2021.100914 |
| Popis: | Surgery is an effective but underused treatment for drug-resistant epilepsy in children. Algorithms to identify surgical candidates and estimate the likelihood of postoperative clinical improvement may be valuable to improve access to epilepsy surgery. We provide a focused review of these approaches. For adults with epilepsy, tools to identify surgical candidates and predict seizure and cognitive outcomes (Ie, Cases for Epilepsy (toolsforepilepsy.com) and Epilepsy Surgery Grading Scale) have been validated and are in use. Analogous tools for children need development. A promising approach is to apply statistical learning tools to clinical datasets, such as electroencephalogram tracings, imaging studies, and the text of clinician notes. Demonstration projects suggest these techniques have the potential to be highly accurate, and await further validation and clinical application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect