Andonegui Navarro, José

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https://academica-e.unavarra.es/handle/2454/48530

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Andonegui Navarro

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José

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Ciencias de la Salud

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0000-0002-3109-3743

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752054

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812298

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2010 - 201922020 - 20251
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    PublicationOpen Access
    Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development
    (Frontiers Media, 2025-06-19) Pinto López, Imanol; Olazarán Santesteban, Álvaro; Jurío, David; Osa Hernández, Borja de la; Sáinz, Miguel; Oscoz, Aritz; Ballaz, Jerónimo; Gorricho Mendívil, Javier.; Galar Idoate, Mikel; Andonegui Navarro, José; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua
    Background: the worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients. Methods: after collecting their requirements, HUN decided to develop a custom AI tool, called NaIA-RD, to assist their GPs in DR screening. This paper introduces NaIA-RD, details its implementation, and highlights its unique combination of DR and retinal image quality grading in a single system. Its impact is measured in an unprecedented before-and-after study that compares 19,828 patients screened before NaIA-RD's implementation and 22,962 patients screened after. Results: NaIA-RD influenced the screening criteria of 3/4 GPs, increasing their sensitivity. Agreement between NaIA-RD and the GPs was high for non-referral proposals (94.6% or more), but lower and variable (from 23.4% to 86.6%) for referral proposals. An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93% of a sample of them as referable. In an autonomous setup, NaIA-RD would have reduced the study visualization workload by 4.27 times without missing a single case of sight-threatening DR referred by a GP. Conclusion: DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening. This shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.
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    PublicationOpen Access
    eOftalmología: estado actual y tendencias futuras
    (Gobierno de Navarra, 2010) Andonegui Navarro, José; Serrano Arriezu, Luis Javier; Eguzkitza Diego, Aitor; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
    La eSalud se puede definir como el uso de las tecnologías de la información y las comunicaciones para proporcionar o apoyar un diverso grupo de actividades relacionadas con la atención en salud. Si este concepto se traslada a la atención oftalmológica sería lo que denominamos como eOftalmología. En este artículo se describe el estado actual de los modelos de eOftalmología en el cribado de la retinopatía diabética y el diagnóstico y el seguimiento del glaucoma crónico y la degeneración macular asociada a la edad. También se definen los requerimientos tecnológicos necesarios para implantar este tipo de modelos de asistencia, se discuten las ventajas derivadas de los mismos y se hace una previsión del impacto que la eOftalmología puede tener en el futuro de la asistencia sanitaria.
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    PublicationOpen Access
    Formalize clinical processes into electronic health information systems: modelling a screening service for diabetic retinopathy
    (Elsevier, 2015-06-14) Eguzkitza Diego, Aitor; Trigo Vilaseca, Jesús Daniel; Martínez de Espronceda Cámara, Miguel; Serrano Arriezu, Luis Javier; Andonegui Navarro, José; Ciencias de la Salud; Osasun Zientziak; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Most healthcare services use information and communication technologies to reduce and redistribute the workload associated with follow-up of chronic conditions. However, the lack of normalization of the information handled in and exchanged between such services hinders the scalability and extendibility. The use of medical standards for modelling and exchanging information, especially dual-model based approaches, can enhance the features of screening services. Hence, the approach of this paper is twofold. First, this article presents a generic methodology to model patient-centered clinical processes. Second, a proof of concept of the proposed methodology was conducted within the diabetic retinopathy (DR) screening service of the Health Service of Navarre (Spain) in compliance with a specific dual-model norm (openEHR). As a result, a set of elements required for deploying a model-driven DR screening service has been established, namely: clinical concepts, archetypes, termsets, templates, guideline definition rules, and user interface definitions. This model fosters reusability, because those elements are available to be downloaded and integrated in any healthcare service, and interoperability, since from then on such services can share information seamlessly.