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Lung cancer is the malignant tumor with the highest mortality rate in the world. Radiotherapy plays an important role in the comprehensive treatment of lung cancer. With the continuous advancement of radiotherapy technology and equipment, it has become one of the effective therapeutic options for lung cancer. In recent years, artificial intelligence technology has developed rapidly and has been widely applied in clinical practice, especially in the diagnosis and treatment of lung cancer imaging. The image database can be obtained by sorting and summarizing the images, which can be used in clinical work and scientific research. In this article, the application of artificial intelligence in lung cancer radiotherapy imaging and lung cancer imaging database was reviewed, aiming to provide reference for the construction of artificial intelligence radiotherapy imaging database for lung cancer.
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The human skeletal muscle drives skeletal movement through contraction. Embedding its functional information into the human morphological framework and constructing a digital twin of skeletal muscle for simulating physical and physiological functions of skeletal muscle are of great significance for the study of "virtual physiological humans". Based on relevant literature both domestically and internationally, this paper firstly summarizes the technical framework for constructing skeletal muscle digital twins, and then provides a review from five aspects including skeletal muscle digital twins modeling technology, skeletal muscle data collection technology, simulation analysis technology, simulation platform and human medical image database. On this basis, it is pointed out that further research is needed in areas such as skeletal muscle model generalization, accuracy improvement, and model coupling. The methods and means of constructing skeletal muscle digital twins summarized in the paper are expected to provide reference for researchers in this field, and the development direction pointed out can serve as the next focus of research.
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Humanos , Tecnología , Simulación por Computador , Bases de Datos Factuales , Movimiento , Músculo EsqueléticoRESUMEN
Radiomics refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with CT, MRI and PET, finding disease biomarkers to increase precision in diagnosis, assessment of prognosis and prediction of therapy response. It is well known that cancer treatment is a great challenge; however, early detection and early treatment can greatly increase the survival rate of patients. The change of tumor cells can be monitored by the examination of gene expression. Moreover, it can also be monitored by imaging markers, which makes Radiomics method widely used in cancer treatment, so Radiomics plays a more and more important role in medical imaging and the related fields. This paper firstly summarizes and analyzes the key problems (including multi-modality image acquisition and reconstruction, image segmentation, feature extraction and qualification, and databases, data sharing, informatics analyses and modeling) to be solved in Radiomics. Different challenges are available in each process. Next, the paper describes the application of Radiomics in detection of non-small cell lung cancer, prostate cancer, breast cancer and other cancers. Finally, taking the rapid development of advanced technologies, the paper puts forward several points of future prediction in terms of the development of Radiomics method.
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Development of a Web-based atlas for collaborative image sharing, processing and analysis of diagnostic images. Materials and Methods: Use of Web 2.0 Personalized Learning Environment tools for social learning and knowledge construction and sharing. Results: The platform allows registered users to upload, visualize, process and comment medical images in a collaborative manner. The system contains a social network module for open case discussion, a video conference and webinar module for real time case analysis, and an image visualization, annotation and processing module for image analysis. The developed open-access platform serves as a large community-created and validated free-repository of diagnostic medical images to be used for training, research as well as reference and second opinion of cases...
Desarrollo de un atlas Web para el intercambio colaborativo, procesamiento y análisis de imágenes diagnósticas. Materiales y Métodos: Uso de herramientas Web 2.0 para desarrollo de Ambientes de Aprendizaje Personalizado para el aprendizaje social y la construcción e intercambio del conocimiento. Resultados: La plataforma permite subir, visualizar, procesar y comentar las imágenes médicas de forma colaborativa. El sistema posee un módulo de red social, módulo de Webinars para análisis de casos en tiempo real, y módulo de visualización, anotación, análisis y procesamiento de imágenes. El sistema tiene usos como un gran repositorio de imágenes diagnósticas creado y validado por la comunidad médica y utilizado para la formación, la investigación, así como referencia y segunda opinión de casos...