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@article{184030,
        author = {Likhitha H Y and Tejaswi H T and Thejaswini L Gowda and Swathi U and Mrs. Megha H C},
        title = {A Comprehensive Survey of Digital Twinning Systems for Medical Imaging of Brain Tumors},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {4105-4123},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184030},
        abstract = {In today's medical environment, quick and accurate diagnosis can make all the difference, and new technologies are pushing the boundaries of what's possible. One exciting development is the combination of AI and digital twin technology to change how we approach brain tumors. This project explores the innovative field of digital twin technology by developing a Digital Twinning System for Medical Imaging—essentially, an intelligent platform that integrates deep learning models with live simulations. Using advanced convolutional neural networks (CNNs) and segmentation tools like U-Net, trained on large datasets from sources like Figshare MRI (Kaggle), BraTS, and TCGA, the system creates personalized virtual models of each patient’s brain that update with new clinical data. Instead of just interpreting static MRI scans, this digital twin captures how tumors change over time, allowing doctors to monitor progress continuously, predict tumor stages, and plan treatments more effectively. By combining AI insights with interactive visual tools, clinicians are enabled with a powerful resource for early detection and smarter interventions. More than a tech breakthrough, this approach aims to change patient care into a more responsive, customized experience—where medical decisions are guided not only by images but by energetic, living models of the human brain.},
        keywords = {Digital Twin, Brain Tumor Imaging, Medical Imaging, Deep Learning, Convolutional Neural Networks (CNN), U-Net, Tumor Segmentation, MRI Analysis, Personalized Healthcare, AI in Medical Diagnosis, Real-time Simulation, Clinical Decision Support, Image-Based Modeling, Tumor Progression Monitoring, Healthcare Digitalization.},
        month = {September},
        }