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Improving epilepsy diagnosis across the lifespan: approaches and innovations.
Pellinen, Jacob; Foster, Emma C; Wilmshurst, Jo M; Zuberi, Sameer M; French, Jacqueline.
Affiliation
  • Pellinen J; Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA. Electronic address: Jacob.Pellinen@cuanschutz.edu.
  • Foster EC; Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia.
  • Wilmshurst JM; Red Cross War Memorial Children's Hospital and University of Cape Town Neuroscience Institute, Cape Town, South Africa.
  • Zuberi SM; Royal Hospital for Children and University of Glasgow School of Health & Wellbeing, Glasgow, UK.
  • French J; Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA.
Lancet Neurol ; 23(5): 511-521, 2024 May.
Article de En | MEDLINE | ID: mdl-38631767
ABSTRACT
Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.
Sujet(s)

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Épilepsie Limites: Humans Langue: En Journal: Lancet Neurol Sujet du journal: NEUROLOGIA Année: 2024 Type de document: Article

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Épilepsie Limites: Humans Langue: En Journal: Lancet Neurol Sujet du journal: NEUROLOGIA Année: 2024 Type de document: Article