Developing an artificial intelligence-based headache diagnostic model and its utility for non-specialists' diagnostic accuracy.
Cephalalgia
; 43(5): 3331024231156925, 2023 05.
Article
em En
| MEDLINE
| ID: mdl-37072919
ABSTRACT
BACKGROUND:
Misdiagnoses of headache disorders are a serious issue. Therefore, we developed an artificial intelligence-based headache diagnosis model using a large questionnaire database in a specialized headache hospital.METHODS:
Phase 1 We developed an artificial intelligence model based on a retrospective investigation of 4000 patients (2800 training and 1200 test dataset) diagnosed by headache specialists. Phase 2 The model's efficacy and accuracy were validated. Five non-headache specialists first diagnosed headaches in 50 patients, who were then re-diagnosed using AI. The ground truth was the diagnosis by headache specialists. The diagnostic performance and concordance rates between headache specialists and non-specialists with or without artificial intelligence were evaluated.RESULTS:
Phase 1 The model's macro-average accuracy, sensitivity (recall), specificity, precision, and F values were 76.25%, 56.26%, 92.16%, 61.24%, and 56.88%, respectively, for the test dataset. Phase 2 Five non-specialists diagnosed headaches without artificial intelligence with 46% overall accuracy and 0.212 kappa for the ground truth. The statistically improved values with artificial intelligence were 83.20% and 0.678, respectively. Other diagnostic indexes were also improved.CONCLUSIONS:
Artificial intelligence improved the non-specialist diagnostic performance. Given the model's limitations based on the data from a single center and the low diagnostic accuracy for secondary headaches, further data collection and validation are needed.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Cefaleia
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Cephalalgia
Ano de publicação:
2023
Tipo de documento:
Article