Publications

Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34

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Author: search.filters.author.Sánchez-Puente, AntonioStart date: 2020

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    Refactoring User Interfaces Through a Data-Driven Framework: a Case Study in the Health Domain
    (IEEE, 2023-10-16) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Antúnez-Muiños, Pablo; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
    User interfaces (UIs) play a crucial role in defining user experiences and influencing the success of software products. While UI design has traditionally been subjective and iterative, data-driven approaches are becoming increasingly popular to ensure that Uis meet user needs and expectations. However, contextual factors such as the application domain can present challenges for designing Uis that are both effective and efficient. This is particularly true in the health domain, where Uis must be adapted to specific tasks and user expertise to maximize the support provided by software systems. Moreover, the urgency of delivering fully functional systems in short periods can relegate UI design to a second plane. This paper presents a framework proposal for refactoring and improving Uis using a data-driven approach, providing an efficient and systematic methodology to address not solved UI issues introduced during previous software development processes. The proposed framework has been successfully applied to two medical platforms, demonstrating the importance of data-driven approaches for UI refactoring in domains with particular necessities.
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    D-AI-COM: A DICOM Reception Node to Automate the Application of Artificial Intelligence Scripts to Medical Imaging Data
    (Springer, 2024-05-01) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Sánchez-Puente, Antonio; Vicente-Palacio, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
    Artificial Intelligence (AI) has proven to be useful in several fields. The medical domain is one of the fields that benefits from the application of AI methods to automate and ease complex tasks including disease detection, segmentation, assessment of organ functions, etc. However, applying these kinds of methods to the variety of data formats involved in health contexts is not trivial. It is necessary to provide technologies that enable non-expert users to benefit from AI applications. This work presents a platform that acts as a DICOM reception node with the goal of automating the application of AI algorithms to medical imaging data. This platform is set to ease the process applying AI to their DICOM images by making the whole process transparent and straightforward for users without AI-related or programming skills.
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    AI-Powered DICOM Image Segmentation: A Collaborative Platform for Continuous Expert Feedback
    (Springer, 2026-03-01) Santos-Blázquez, Pablo; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, P. L.
    his work presents the development of an interactive web platform that integrates deep learning techniques for the segmentation of cardiac ultra-sound (echocardiogram) images. The platform incorporates a Picture Archiving and Communication System (PACS) to facilitate the seamless visualization, anno-tation, and automated processing of DICOM images. The web platform features an intuitive interface that allows healthcare professionals to interactively annotate medical images, providing feedback that directly informs model improvements. The system’s retraining workflow ensures that AI-driven segmentation remains adaptable to real-world clinical needs. These findings underscore the importance of iterative AI model refinement through expert feedback, paving the way for more reliable and personalized medical image analysis.
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    From spreadsheets to interfaces: redesigning clinical variable definition through interactive workflows
    (CEUR-WS.org, 2025-09-03) Vázquez-Ingelmo, Andrea; Nieto-Campo, Islem Román; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, Pedro L.
    Spreadsheets remain a common but fragile foundation for clinical data management, often leading to errors and inefficiencies in defining and collecting structured variables. This paper presents a user-centered redesign of the variable definition workflow in a platform for managing structured clinical data and medical images. The proposed solution replaces manual spreadsheet-based schema creation with an interactive web interface that enables users to define, categorize, and reuse variables more effectively. It also introduces automated generation of validated spreadsheet templates based on the platform’s internal schema, reducing the likelihood of formatting and semantic errors during data entry. A revised workflow illustrates the improved process, and the system addresses key usability issues previously identified through heuristic evaluations. Remaining limitations, such as continued reliance on offline data entry, are discussed, along with future work directions that include usability validation and AI-assisted variable generation.