Artificial intelligence in trigeminal neuralgia: trigeminal nerve segmentation and neurovascular conflict detection: a systematic review and meta-analysis.
Neurosurg RevSep 14, 2026 (epub)
Mohammadamin Sabbagh Alvani, Ibrahim Mohammadzadeh, Bardia Hajikarimloo et al.
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In 30 seconds
This systematic review and meta-analysis evaluated the effectiveness of artificial intelligence (AI) models in diagnosing trigeminal neuralgia (TN) through MRI. Analyzing data from 5 studies involving 577 patients, the pooled sensitivity of AI models for detecting TN was 71%, with a specificity of 92%. These results indicate that AI can significantly enhance diagnostic accuracy in TN management.
Key findings
- Pooled sensitivity of AI models in detecting TN was 71% (95% CI: 65-76%).
- Specificity of AI models reached 92% (95% CI: 88-94%).
- The diagnostic odds ratio (DOR) was 26.71 (95% CI: 16.38-43.56).
- Area under the curve (AUC) for AI models was 0.91 (95% CI: 0.88-0.93).
Why it matters
Accurate diagnosis of trigeminal neuralgia is essential for effective treatment planning. The integration of AI in diagnostic processes may enhance precision, leading to improved patient outcomes and more informed clinical decisions.