Current Progress of Digital Twin Construction Using Medical Imaging

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 investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine.