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. 2023 Aug 1;80(8):805-812.
doi: 10.1001/jamaneurol.2023.1645.

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence

Jesper Tveit  1 , Harald Aurlien  1   2 , Sergey Plis  3 , Vince D Calhoun  3 , William O Tatum  4 , Donald L Schomer  5 , Vibeke Arntsen  6 , Fieke Cox  7 , Firas Fahoum  8 , William B Gallentine  9 , Elena Gardella  10   11 , Cecil D Hahn  12   13 , Aatif M Husain  14   15 , Sudha Kessler  16   17   18 , Mustafa Aykut Kural  19   20 , Fábio A Nascimento  21 , Hatice Tankisi  19   20 , Line B Ulvin  22 , Richard Wennberg  23 , Sándor Beniczky  10   19   20
Affiliations

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence

Jesper Tveit et al. JAMA Neurol. .

Abstract

Importance: Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed.

Objective: To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse.

Design, setting, and participants: In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded.

Main outcomes and measures: Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording.

Results: The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts.

Conclusions and relevance: In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Tveit reported being Senior Business Intelligence Developer in Holberg EEG; Holberg EEG received grants from SkatteFUNN R&D, a tax incentive government program; Natus Medical was a minority shareholder in Holberg EEG during the conduct of the study. Dr Aurlien reported being Chief Medical Officer and minority shareholder in Holberg EEG; Holberg EEG received grants from SkatteFUNN R&D, a tax incentive government program; Natus Medical was a minority shareholder in Holberg EEG during the conduct of the study. Dr Plis reported receiving consulting fees from Holberg EEG during the conduct of the study. Dr Gallentine reported receiving personal fees from Holberg EEG during the conduct of the study. Dr Hahn reported receiving personal fees from Holberg EEG during the conduct of the study and grants and personal fees from Takeda and UCB outside the submitted work. Dr Husain reported receiving personal fees from UCB, Jazz Pharma, Merck, EISAI, Marinus Pharmaceuticals, Pipeline Therapeutics, Neurelis, Springer Publishing, Demos Medical Publishing, and Wolters Kluwer outside the submitted work. Dr Ulvin reported that Oslo University Hospital was compensated by Holberg EEG during the conduct of the study. Dr Beniczky reported receiving personal fees from Natus Medical, UNEEG Medical, EISAI, and UCB outside the submitted work. No other disclosures were reported.

Figures

Figure.
Figure.. Receiver Operating Characteristics Curves on the Holdout Test EEG Data Set (n = 2549)
AUC indicates area under the curve.
See this image and copyright information in PMC

Comment in

References

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