Table 6. Previous reports on AI diagnosis for headache disorders.
Abbreviations: AI, artificial intelligence; LGBM, light gradient boosting machine; TACs, trigeminal autonomic cephalalgias; TTH, tension-type headache; MOH, medication-overuse headache. †, calculated in macro-average.
| Author | Year | Output by the model | Methods | Variables | Training sample number | Test sample number | Validation sample number | %Migraine | Accuracy | Sensitivity (recall) | Specificity | Precision | F-value |
| Yin [55] | 2015 | 2 class; Migraine or TTH | Case-based reasoning + Genetic algorithm | 81 | 676 | 222 | Not performed | 76.10% | 93.00% | 97.02% | 79.20% | 93.14% | 95.04% |
| Walters [54] | 2016 | 2 class; Migraine or Other headache disorders | Logistic regression | 4 | 887 | 942 | Not performed | 9.40% | 92% | 94% | 92% | 64% | 93% |
| Vandewiele [56] | 2018 | 3 class; Migraine, TTH, TACs | Decision tree | Not described | 849 | - | 32 | Not described | 98% | 98% | 98% | Not described | Not described |
| Kwon [57] | 2020 | 5 class; Migraine, TTH, TACs, Thunderclap headache, Epicranial headache | eXtreme Gradient Boosting, | 75 | 1286 | 876 | Not performed | 68.49% | 58.60%† | 58.70%† | 85.64%† | 65.28%† | 58.64%† |
| Cowan [58] | 2022 | 2 class; Migraine or Other headache disorders | Decision tree | 135 | - | - | 212 | 62% | 92% | 89% | 97% | 98% | 93% |
| Katsuki [10] | 2022 | 5 class; Migraine or MOH, TTH, TACs, Other primary headaches, Secondary headaches | Light gradient boosting machine | 17 | 2800 | 1200 | 50 | 60.00% | 90.00% | 68.57% | 95.00% | 96.43% | 88.08% |
| Katsuki [60] | 2023 | 5 class; Migraine or MOH, TTH, TACs, Other primary headaches, Secondary headaches | Gradient boosting classifier | 22 | 4240 | 1818 | Not performed. | 79.7% | 93.7%† | 40.6%† | 48.5%† | 88.7%† | 43.5%† |
| This study | 2023 | 2 class; Migraine or Other headache disorders | Extremely randomized trees | 14 | 636 | 273 | Not performed. | 26.3% | 94.5% | 88.7% | 96.5% | 90.0% | 89.4% |