Developing an artificial intelligence-based headache diagnostic model and its utility for non-specialists' diagnostic accuracy. (May 2023)

• Record Type: Journal Article
• Authors: Katsuki, Masahito; Shimazu, Tomokazu; Kikui, Shoji; Danno, Daisuke; Miyahara, Junichi; Takeshima, Ryusaku; Takeshima, Eriko; Shimazu, Yuki; Nakashima, Takahiro; Matsuo, Mitsuhiro; Takeshima, Takao
• Is Part Of: Cephalalgia. Volume 43:Number 5(2023)
• Publication Date: 2023-05
• Subjects: Coronavirus disease 2019 (COVID-19) -- machine learning -- migraine -- smartphone application -- telemedicine; Headache -- Periodicals; 616.8491
• DOI: 10.1177/03331024231156925
• Languages: English
• ISSNs: 0333-1024

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.