Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods
Bibliographic record
Abstract
Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS-2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty-eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F-2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.213 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".