AI in epilepsy neuroimaging
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review without a specific PICO framework |
| I — Intervention | Not applicable - narrative review format |
| C — Comparator | Not applicable - narrative review format |
| O — Outcomes | Not applicable - narrative review format |
Bottom Line
This narrative review summarizes current AI applications in epilepsy neuroimaging, covering lesion detection, seizure localization, and surgical planning tools. While the field shows promise with various machine learning approaches from neural networks to graph-based methods, most applications remain in early development stages. The authors acknowledge significant clinical integration hurdles that must be addressed before widespread adoption. For Australian clinicians, these tools may enhance diagnostic capabilities in specialized epilepsy centres, but robust validation studies and regulatory approval will be essential before clinical implementation. The review highlights the gap between research developments and clinical readiness, emphasizing the need for systematic evaluation of these emerging technologies in real-world epilepsy care settings.
Key Findings
Effect Size: Not quantified - narrative synthesis only
Primary Outcome: Multiple AI applications identified across epilepsy neuroimaging spectrum
Nnt Or Sensitivity: Performance metrics not synthesized quantitatively
Confidence Interval: Not provided
Clinical Application
Limited by early developmental stage of most tools and clinical integration challenges Relevant to Australian epilepsy centres with advanced neuroimaging capabilities. Integration would require TGA approval for clinical decision-making tools and consideration of Medicare funding for enhanced imaging protocols. Patients with epilepsy undergoing neuroimaging evaluation, particularly those being considered for surgical intervention
Abstract
PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability of neuroimaging datasets has fuelled a rapid expansion in AI applications to epilepsy neuroimaging. This review summarizes the main applications of AI in epilepsy neuroimaging and suggests future directions for the field. RECENT FINDINGS: A range of different machine learning approaches, from multi-layer perceptrons to volumetric and graph-based convolutional neural networks, have been utilized for prediction of whether people will have epilepsy, detection of structural epilepsy lesions, localization of seizure onset zones, segmentation of resection cavities after epilepsy surgery as well as for image enhancement. SUMMARY: AI in epilepsy neuroimaging research has primarily focussed on lesion detection and localization, with a number of open and validated tools now available for evaluation across diverse settings. Additional applications of AI in epilepsy neuroimaging are either at earlier stages of development or emerging as new challenges. As these tools and their supporting evidence mature, further work addressing the hurdles of clinical integration is required.
References
- 1.Adler, S., & Wagstyl, K. (2026). AI in epilepsy neuroimaging. Current Opinion in Neurology, 39(2). https://doi.org/10.1097/WCO.0000000000001465
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