Jonathan J Halford1, M Brandon Westover2, Suzette M LaRoche3, Micheal P Macken4, Ekrem Kutluay1, Jonathan C Edwards1, Leonardo Bonilha1, Giridhar P Kalamangalam5, Kan Ding6, Jennifer L Hopp7, Amir Arain8, Rachael A Dawson1, Gabriel U Martz9, Bethany J Wolf10, Chad G Waters11, Brian C Dean11. 1. Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, U.S.A. 2. Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A. 3. Mission Health, Asheville, North Carolina, U.S.A. 4. Department of Neurology, Northwestern University, Chicago, Illinois, U.S.A. 5. Department of Neurology, University of Florida, Gainesville, Florida, U.S.A. 6. Department of Neurology, University of Texas Southwestern, Dallas, Texas, U.S.A. 7. Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, U.S.A. 8. Department of Neurology, Vanderbilt University, Nashville, Tennessee, U.S.A. 9. Norton Neurology Services, Louisville, Kentucky, U.S.A. 10. Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, U.S.A. 11. School of Computing, Clemson University, Clemson, South Carolina, U.S.A.
Abstract
OBJECTIVE: The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings. METHODS: Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison. RESULTS: Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts. CONCLUSIONS: Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.
OBJECTIVE: The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings. METHODS: Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison. RESULTS: Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts. CONCLUSIONS: Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.
Authors: Elham Bagheri; Justin Dauwels; Brian C Dean; Chad G Waters; M Brandon Westover; Jonathan J Halford Journal: Clin Neurophysiol Date: 2017-07-18 Impact factor: 3.708
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Authors: Nitish M Harid; Jin Jing; Jacob Hogan; Fábio A Nascimento; An Ouyang; Wei-Long Zheng; Wendong Ge; Sahar F Zafar; Jennifer A Kim; D Lam Alice; Aline Herlopian; Douglas Maus; Ioannis Karakis; Marcus Ng; Shenda Hong; Zhu Yu; Peter W Kaplan; Sydney Cash; Mouhsin Shafi; Gabriel Martz; Jonathan J Halford; Michael Brandon Westover Journal: Epileptic Disord Date: 2022-06-01 Impact factor: 2.333