Automated detection of trigeminal neuralgia using multi-domain EEG feature analysis and CNN-attention architecture: a comparative machine learning study.
EEG-based AI detected trigeminal neuralgia patterns non-invasively, potentially reducing misdiagnosis and treatment delays.
Trigeminal neuralgia (TN) is a debilitating neuropathic pain disorder characterized by sudden, intense facial pain, with diagnosis heavily reliant on subjective symptom reporting, leading to frequent misdiagnosis and delayed treatment. This proof-of-concept study demonstrates that EEG-based automated detection using a CNN with Attention architecture is a promising non-invasive approach for TN, though external validation on large independent cohorts is required before conclusions on diagnostic generalizability can be established.
What the study was
- Study design
- Cohort study
- Population
- Cancer/disease patients
- Sample size
- 72
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Scientific reports
Why it surfaced
Matched topic(s): AI/ML in clinical diagnostics and imaging. Study design: Cohort study. Score 6/10.
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