Evidence map›Paper›PMID 42531864›Full record

ArticleAtencion primaria2026

[Development and validation of Cefalytics: an artificial intelligence-based application for the rapid diagnosis of primary headaches].

Josefina Martínez Simón, Pablo Arjona Ruiz, Jesús Martín Alcalá, Juan Manuel García-Torrecillas, María Fernández Recio, Antonio Arjona-Padillo

Abstract readEnglish Abstract
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Article in Atencion primaria, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Josefina Martínez SimónServicio de Neurología, Hospital Universitario Torrecárdenas, Almería, España.
Pablo Arjona RuizMáster en Big Data.
Jesús Martín AlcaláMáster en Inteligencia Artificial.
Juan Manuel García-TorrecillasServicio de Urgencias y Unidad de Investigación Biomédica, Hospital Universitario Torrecárdenas, Almería; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, España.
María Fernández RecioServicio de Neurología, Hospital Virgen del Valme, Sevilla, España.
Antonio Arjona-PadilloServicio de Neurología, Hospital Universitario Torrecárdenas, Almería, España. Electronic address: aarjonap@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo design and validate a Spanish-language artificial intelligence-based application to support the diagnosis of primary headaches by non-expert physicians.

designRetrospective observational study for diagnostic development and validation based on clinical records.

settingNeurology outpatient clinics receiving patients referred from primary care (level 2 headache care).

participantsA total of 1,029 adult patients with a diagnosis of 17 types of primary headache confirmed by an expert neurologist according to the criteria of the International Classification of Headache Disorders, 3rd edition (ICHD-3), treated at two hospital centers between 2022 and 2024 were included.

main outcome measuresSensitivity, specificity, F1-score and accuracy of the rule-based diagnostic algorithm and the supervised learning model.

resultsThe application integrates a structured clinical questionnaire that includes most primary headache diagnoses defined in the ICHD-3. The rule-based algorithm achieved a sensitivity of 89.8% (95% CI: 87.9-91.5), specificity of 99.4% (95% CI: 99.2-99.5), F1-score of 91.8% (95% CI: 90.3-93.1), and accuracy of 93.3% (95% CI: 91.5-94.8). The supervised learning model showed a sensitivity of 88.1% (95% CI: 84.0-91.8), specificity of 99.1% (95% CI: 98.6-99.5), F1-score of 88.5% (95% CI: 84.6-92.0), and accuracy of 85.5% (95% CI: 79.3-91.4).

conclusionsCefalytics (https://cefalytics.com/) shows high diagnostic performance and may represent a useful support tool for non-expert physicians, particularly in primary care, facilitating greater diagnostic accuracy and earlier therapeutic optimization. These results require external validation in an independent cohort within this setting.

Indexed as

Aprendizaje automáticoArtificial IntelligenceCefalea primariaDiagnosisDiagnósticoInteligencia artificialMachine LearningMigraineMigrañaPrimary Headache

Identifiers

PMID42531864
PMCPMC13452188

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.