Current Progress of Digital Twin Construction Using Medical Imaging
Feng Zhao, Yizhou Wu, Mingzhe Hu, Chih-Wei Chang, Ruirui Liu, Richard, Qiu, Xiaofeng Yang

TL;DR
This paper reviews how advances in medical imaging are crucial for developing digital twins in healthcare, highlighting recent progress, challenges, and future research directions to improve personalized medicine.
Contribution
It provides a comprehensive overview of the role of medical imaging in digital twin construction, emphasizing recent technological advances and remaining challenges.
Findings
Imaging accuracy improvements enhance digital twin diagnostics.
Advances in machine learning improve model precision.
Technical barriers still limit clinical application.
Abstract
Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data, such as MRI, CT, PET, and ultrasound, with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review…
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Taxonomy
TopicsDigital Transformation in Industry
