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This review article examines various cadaveric preservation techniques, ranging from traditional formalin fixation to modern formaldehyde-free methods incorporating machine learning, nanotechnology, and 3D imaging. It assesses the advantages and disadvantages of each technique, focusing on their impact on tissue quality, molecular analysis, and suitability for surgical training and research. The authors propose a decision-making framework to guide the selection of preservation methods based on instructional goals, simulation needs, and ethical considerations.
Emerging cadaveric preservation techniques offer improved tissue quality and molecular integrity compared to traditional formalin fixation, enhancing their utility for surgical simulation and advanced anatomical research.
Cadaveric preservation is fundamental to medical education, research, and surgical training, offering unmatched understanding of human anatomy and tissue dynamics. Although formalin fixation facilitates extended specimen preservation, its toxicity, tissue rigidity, and disruption of molecular analysis have prompted the creation of safer, more biologically representative alternatives. This review delineates the progression of cadaveric preservation, spanning from classical formaldehyde-based techniques through transitional low-toxicity chemical approaches to emerging formaldehyde-free methodologies. We assess the composition, benefits, and drawbacks of each technique, emphasizing the integration with machine learning-guided perfusion, nanotechnology-enhanced polymers, and hybrid approaches that combine digital imaging, 3D scaffolds, and automated monitoring. We propose a decision-making framework that integrates preservation decisions with instructional goals, surgical simulation needs, and research priorities, while adhering to ethical and environmental standards. This thorough analysis combines classic and innovative methodologies to provide practical suggestions for institutions aiming to enhance cadaveric resources for future medical professionals.
