Generalizability of electroencephalographic interpretation using artificial intelligence : An external validation study
Mansilla, D. (Institute of Neurosurgery Dr. Asenjo)
Tveit, J. (Holberg EEG)
Aurlien, H. (Holberg EEG)
Avigdor, T. (Duke University Medical Center)
Ros-Castelló, Victoria (Institut de Recerca Sant Pau)
Ho, A. (Duke University Medical Center)
Abdallah, C. (Montreal Neurological Institute and Hospital)
Gotman, J. (McGill University)
Beniczky, S. (Aarhus University Hospital (Aarhus, Dinamarca))
Frauscher, B. (Duke Pratt School of Engineering)
Universitat Autònoma de Barcelona

Data:	2024
Resum:	The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings. We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard. We analyzed EEGs from 104 patients (64 females, median age = 38. 6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12. 2%], false classification during sleep: 2/11 [18. 2%]; p =. 63) and normal variants (false classification in presence of normal variants: 4/14 [28. 6%], false classification in absence of normal variants: 3/38 [7. 9%]; p =. 07). SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Automatic EEG analysis ; Epilepsy ; Normal variants ; Routine EEG ; Sleep
Publicat a:	Epilepsia, Vol. 65 Núm. 10 (october 2024) , p. 3028-3037, ISSN 1528-1167

DOI: 10.1111/epi.18082
PMID: 30141002

10 p, 2.1 MB

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