Digital twin-assisted surgery: technological architecture, integration across surgical phases, implementation...

Area of Science:

• Medical Technology
• Surgical Innovation
• Digital Health

Background:

• Digital twin-assisted surgery employs dynamic, patient-specific virtual models mirroring patients in real-time.
• This approach integrates data from robotics, implants, biosensors, and imaging for continuous updates.
• It aims to enhance surgical planning, intraoperative guidance, and postoperative recovery.

Purpose of the Study:

• To provide a comprehensive analysis of digital twin technology in surgery.
• To explore its integration across preoperative, intraoperative, and postoperative phases.
• To critically evaluate implementation challenges and propose future directions.

Main Methods:

• Review of conceptual foundations and technological architecture of digital twins in surgery.
• Analysis of real-world integration and applications.
• Evaluation of technical, practical, and ethical implementation challenges.

Main Results:

• Digital twins offer predictive modeling, real-time decision support, and personalized recovery.
• Key challenges include data interoperability, sensor accuracy, real-time processing, costs, infrastructure, training, regulatory standards, ethics, and global equity.
• Proposed solutions involve AI, edge computing, standardized data frameworks, and public-private partnerships.

Conclusions:

• Digital twin-assisted surgery is poised to become a cornerstone of precision surgical care.
• Overcoming implementation challenges is crucial for widespread adoption.
• Future directions focus on technological advancements and collaborative efforts to ensure equitable access and efficacy.