Toward unified and comprehensive automated electroencephalogram interpretation: a multicentre development and validation of an electroencephalogram foundation model.
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Sun Chenxi, Karakis Ioannis, Herlopian Aline, Ng Marcus C, Osman Gamaleldin, Sheikh Zubeda, Taraschenko Olga, Yoo Ji Yeoun, Appavu Brian L, Arcot Jayagopal Lakshman, Kaplan Peter W, Lee Jong Woo, Selioutski Olga, Haider Hiba A, Halford Jonathan J, Hoch Daniel B, Lam Alice, Nascimento Fabio A, Pathmanathan Jay, Schmitt Sarah, De Stefano Pia, Fisch Urs, Gijs Jeroen, Castro-Lima Humberto, Kong Wan-Yee, Cervenka Mackenzie C, Dhakar Monica B, Fatima Safoora, Gaspard Nicolas, Gilmore Emily J, Herman Susan T, Holmes Manisha G, Johnson Emily L, Muniz Carlos F, Rosenthal Eric S, Rodriguez Ruiz Andres A, Sarkis Rani A, Shafi Mouhsin M, Swisher Christa B, Tabaeizadeh Mohammad, Benbadis Selim R, Chan Fonda, Chu Catherine J, Dolatshahi Marjan, Greenblatt Adam S, Katyal Roohi, Nwankwo Chinasa, Amorim Edilberto, Tatum William O, Weber Dan, Loddenkemper Tobias, Peters Jurriaan M, Katwa Umakanth, Maski Kiran, Thomas Robert Joseph, Hong Shenda, Yuan Doyle, Cash Sydney S, Cole Andrew J, Goldenholz Daniel M, Stow Charlotte, Kim Jennifer A, Zafar Sahar F, Struck Aaron F, Westover M Brandon, Jing Jin
Аннотация
METHODS: In this multicentre study we developed a multidomain omnibus for reading and generalising over thorough EEG interpretation (MORGOTH), a foundation model that supports broad clinical interpretation across all major settings. We developed MORGOTH using EEGs from 18 677 patients across Massachusetts General Hospital, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center, and Boston Children's Hospital, collected between Jan 1, 2003, and Feb 1, 2025, and validated it internally on 13 334 patients and externally on 1573 patients from 48 institutions, spanning diverse clinical settings and ages (0 years to >90 years). Test datasets annotated by six to 30 experts enabled inter-rater reliability (IRR) analysis by comparing model-expert and expert-expert agreement. MORGOTH was compared against both human experts and state-of-the-art models using area under the curve (AUC) and the percentage of experts' operating points under the curve (EUC) for receiver operating characteristic (ROC) and precision-recall curves, as well as IRR and statistical calibration.
FINDINGS: MORGOTH achieved expert-level performance with AUC-ROC scores of 0·86 to 0·98 across 17 EEG findings. MORGOTH outperformed at least 90% of experts on three of seven multi-expert-annotated datasets and exceeded at least 20% of experts on each of the 17 tasks. Event-level performance was especially strong for seizure and ictal-interictal-injury continuum detection (EUC=96·6%) and spike detection (EUC=100%). IRR analysis showed that MORGOTH matched or exceeded expert consensus. External validation confirmed consistent performance with modest declines from internal to external test sets (event-level AUC -1·21%, EUC -3·33%; EEG-level AUC -2·12%, and EUC -9·52%). MORGOTH performance also remained robust across age, sex, and moderate channel loss, with lower age sensitivity (20·90% vs 30·90%) and fewer sex-related differences (33·33% vs 44·00%) than SPaRCNet.
INTERPRETATION: MORGOTH advances automated EEG interpretation with expert-level performance across clinical settings, offering improved diagnostic accuracy in low-resource environments and greater efficiency in high-volume centres.
FUNDING: US National Institutes of Health.