GRIAL resources

Permanent URI for this communityhttps://repositorio.grial.eu/handle/123456789/1

Browse

Filters

2024 - 20264

Settings

Author: search.filters.author.Vázquez-Ingelmo, AndreaSubject: search.filters.subject.DICOM

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Management and Application of AI to DICOM Image Processing: A Systematic Mapping Literature Review
    (Springer, 2024-07-15) Fraile-Sanchón, Rubén; Vázquez-Ingelmo, Andrea; García-Peñalvo, Francisco José; García-Holgado, Alicia
    Artificial intelligence (AI) has the potential to bring unprecedented benefits to humankind. Therefore, it is worth investigating how to maximize these benefits while avoiding potential pitfalls. Given this context, the first task necessary to assess the potential of this approach is to understand the management landscape and the application of AI to DICOM image processing. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. 35 studies have been selected from a total of 154 analyzed. From them, in addition to obtaining a clear view of the application of AI to DICOM images, we can also conclude that pre-trained AI algorithms are used in a higher amount than non-trained algorithms in terms of DICOM image usage.
  • Thumbnail Image
    Item
    Desarrollo de un Sistema Interactivo de Interconexión y Análisis de Imágenes DICOM con Retroalimentación Médica
    (Departamento de Informática y Automática. Universidad de Salamanca, 2025-03-19) Santos Blázquez, Pablo; García-Peñalvo, Francisco José; Vázquez-Ingelmo, Andrea
    La segmentación precisa de estructuras cardíacas en imágenes médicas es crucial para un diagnóstico y tratamiento efectivos. Este pro-yecto presenta el desarrollo de "dAIcom", una plataforma web interactiva para la visualización, anotación y análisis de imágenes DICOM (Digital Imaging and Communication in Medicine), mejorando la colaboración multidisciplinaria y la retroalimentación médica. Para ello, se ha integra-do un modelo de inteligencia artificial basado en la arquitectura UNET, entrenado específicamente para segmentar estructuras cardíacas como el ventrículo izquierdo, la aurícula izquierda y el miocardio en imágenes obtenidas mediante el uso de ultrasonidos (ecocardiografías). Con el fin de evaluar el rendimiento y la adaptabilidad del modelo, se han reali-zado tres experimentos de reentrenamiento. Los resultados revelan que un mayor número de imágenes y anotaciones mejora significativamente la capacidad de generalización del modelo, reduciendo el sobreajuste. Además, se desarrolla un sistema PACS (Picture Archiving and Commu-nication System) para gestionar y almacenar eficientemente las imágenes DICOM, asegurando la interoperabilidad y el almacenamiento seguro de los datos médicos. A lo largo del trabajo se han identificado diversos desafíos, incluyendo dificultades para obtener imágenes médicas debido a restricciones de confidencialidad y limitaciones computacionales. El trabajo futuro se centra en ampliar el conjunto de datos, optimizar los hiperparámetros y mejorar el proceso de anotación para incrementar la robustez y relevancia clínica del sistema. Estos esfuerzos buscan asegurar que la plataforma "dAIcom"no solo sea tecnológicamente avanzada, sino también clínicamente útil para mejorar la atención médica.
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported