2022
DOI: 10.1111/epi.17206
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Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts

Abstract: Objective To evaluate the diagnostic performance of artificial intelligence (AI)–based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20‐min) electroencephalography (EEG) recordings. Methods We evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED definition to assess the automated detections grouped into clusters by the algorithms. We used three previously developed AI algorithms: Encevis, Sp… Show more

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Cited by 37 publications
(28 citation statements)
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“…Refs. 86 , 87 ) would ease the objective comparison of the performance of both MEG systems, yet this was beyond the scope of the current manuscript.…”
Section: Discussionmentioning
confidence: 99%
“…Refs. 86 , 87 ) would ease the objective comparison of the performance of both MEG systems, yet this was beyond the scope of the current manuscript.…”
Section: Discussionmentioning
confidence: 99%
“…Use of more sophisticated algorithms for the identification of interictal abnormalities (e.g. da Silva Lourenço et al (2021); Kural et al (2022)) would ease the objective comparison of the performance of both MEG systems, yet this was beyond the scope of the current manuscript.…”
Section: Limitations On Identification Of Iedsmentioning
confidence: 97%
“…These queries may be used "in the loop" or "out of the loop". With the former, clinicians may need to modify treatment options based on data that the BCI reads; e.g., confirming that Hull has correctly detected seizures and responded appropriately [37,38]. The latter refers to interactive queries used by technicians to debug system operations, by clinicians to glean the individual's medical history, and by researchers to better understand brain function.…”
Section: Introductionmentioning
confidence: 99%