RESUMO
El campo de la radiología ha experimentado avances notables en las últimas décadas, con desarrollos que van desde la mejora de la calidad y digitalización de las imágenes hasta la detección asistida por computadora. Particularmente, la aparición de técnicas de Inteligencia Artificial basadas en Deep Learning y Visión Computacional han promovido soluciones innovadoras en el diagnóstico y el análisis radiológico. Se explora la relevancia de los desarrollos y modelos open source en el progreso de estas técnicas, resaltando el impacto que la colaboración y el acceso abierto han tenido en el avance científico del campo. La investigación tiene un enfoque cualitativo, con alcance descriptivo y retrospectivo, de corte longitudinal. Se realizó un análisis documental de la evolución y el impacto del open source en la Radiología, poniendo de relieve la colaboración multidisciplinar. Se examinaron casos de uso, ventajas, desafíos y consideraciones éticas en relación con la implementación de soluciones basadas en Inteligencia Artificial en Radiología. El enfoque open source ha mostrado ser una influencia positiva en la Radiología, con potencial para influir en la atención médica, ofreciendo soluciones más precisas y accesibles. No obstante, se presentan desafíos éticos y técnicos que requieren atención.
The field of radiology has seen notable advances in recent decades, with developments ranging from image quality improvement and digitization to computer-aided detection. Particularly, the emergence of Artificial Intelligence techniques based on Deep Learning and Computer Vision have promoted innovative solutions in diagnosis and radiological analysis. This article explores the relevance of open source developments and models in the progress of these techniques, highlighting the impact that collaboration and open access have had on the scientific advancement in this field. This research has a qualitative approach, with a descriptive, retrospective, longitudinal scope. A documentary analysis of the evolution and impact of open source in Radiology was carried out, highlighting multidisciplinary collaboration. Use cases, advantages, challenges and ethical considerations were also examined in relation to the implementation of AI-based solutions in Radiology. The Open Source approach has been shown to be a positive influence in Radiology, with the potential to influence medical care, offering more precise and accessible solutions. However, there are ethical and technical challenges that require attention.
RESUMO
Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.
