Public articles linked to the same research event.
Neurosurgical Review This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.
This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.
This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.
This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.