Current progress of digital twin construction using medical imaging.

Area of Science:

• Biomedical Engineering
• Medical Informatics
• Radiology

Background:

• Medical imaging is crucial for developing patient-specific digital twins.
• Advanced imaging modalities like MRI, CT, PET, and ultrasound are integrated with computational frameworks.
• Digital twins enable real-time simulation, predictive modeling, and early disease detection for personalized care.

Purpose of the Study:

• To classify methodologies for medical imaging in digital twin technology.
• To provide evidence of advanced imaging's impact on diagnostic accuracy, treatment effectiveness, and patient outcomes.
• To identify technical bottlenecks and future research directions for digital twin applications in precision medicine.

Main Methods:

• System-by-system classification of digital twin methodologies.
• Review of evidence demonstrating the benefits of advanced imaging modalities.
• Analysis of technical barriers and future directions, including AI-driven solutions.

Main Results:

• Advanced imaging modalities significantly enhance the accuracy and clinical utility of digital twins.
• Digital twins informed by medical imaging improve individualized treatment planning and patient outcomes.
• Key challenges include complex anatomical modeling, multimodal integration, data scarcity, and computational demands.

Conclusions:

• Medical imaging is a cornerstone of digital twin technology, driving advancements in precision medicine.
• Despite challenges, ongoing innovations in imaging and machine learning are unlocking the potential of digital twins for enhanced healthcare.
• Future directions focus on AI-driven data augmentation and real-time model optimization to overcome current limitations.