[Methods, applications, and future perspectives of intraoperative tissue identification]

Area of Science:

• Biomedical Engineering
• Surgical Technology
• Medical Imaging

Context:

• Technological advancements over the past century have focused on intraoperative tissue identification and differentiation.
• Minimally invasive medicine relies on precise identification of target structures while preserving adjacent tissues.
• Oncology benefits significantly from technologies that identify or classify neoplastic tissue, improving cancer surgery outcomes, such as in breast cancer operations.

Purpose:

• To review and assess the utility and application of various technologies for intraoperative tissue identification.
• To highlight the evolution from ex vivo to in situ and asynchronous to real-time clinical assistance.
• To explore the integration of Artificial Intelligence (AI) in refining existing and developing new methods for enhanced surgical accuracy.

Summary:

• Technologies leverage distinct physical tissue characteristics like induced fluorescence, optical coherence, and electrical impedance.
• Recent developments emphasize real-time, in situ assistance, exemplified by optical emission spectroscopy.
• Future refinements involve AI integration to boost performance and minimize inter-operator variability in surgical procedures.

Impact:

• Improved surgical precision and patient outcomes through enhanced tissue differentiation.
• Facilitation of minimally invasive surgical techniques with greater confidence.
• Potential for AI-driven tools to standardize and elevate the quality of surgical interventions across different operators.