Barone Allie Kendall

Background: Traumatic injuries take the lives of 4.4 million people around the world each year and constitute nearly 8% of all deaths worldwide [1]. Access to immediate and comprehensive surgical care is paramount to patient survival, but even with timely and well-resourced management, trauma surgery outcomes suffer from the time-sensitive and unpredictable nature of these cases. Up to 39 percent of trauma patients have injuries that are initially missed, and up to 22 percent of those injuries are clinically significant [2]. There are also increasingly fewer providers equipped to manage complex surgical trauma, and surgical trainees are reporting less exposure to operative trauma across the globe, which bodes especially poorly for austere regions where general surgeons are expected to provide broad and comprehensive care [3]. Three-dimensional (3D) modeling applications have been increasingly prevalent in medicine, particularly in surgery, and have shown improved patient outcomes and surgeon confidence in many different uses and specialties [4-12]. This review investigates potential 3D modeling applications in complex surgical trauma that could bridge some of the gaps in the field to improve patient outcomes and reduce the number of preventable deaths related to trauma.

Methods: Literature review consisted of PubMed searches such as “3D modeling in complex trauma,” “(3D modeling) AND (trauma surgery),” and “(modeling) AND (acute trauma),” “(virtual model) AND (surgical training),” and “(3D modeling) AND (penetrating trauma)”

Results: 3D models are widely accepted and used regularly in many surgical specialties for surgical education, pre-operative planning, visualization, and customized implants and tools, and have seen improved patient outcomes in decreased operative time, increased surgeon confidence, and verification of clinical course [4-12]. However, none of these use cases meet the stringent time requirements of an unstable trauma patient [13]. 3D modeling has also been used to predict traumatic injuries, in combination with Bayesian networks [14,15], rather than just visualizing them. Newer physics-based anatomical models [16,17] and incorporation of AI based injury detection [18] bring new possibilities to that ability.

Conclusions: 3D modeling tools, if implemented in a manner that is rapid and accurately predictive of anatomy and clinical course, could potentially fill the gaps in global access to trauma care, fill gaps in surgical education and experience, and fill gaps of patient care and outcomes. Future innovations and applications in surgical education, clinical decision making, and physics-based models for a variety of tissues and organs are still needed.

Works Cited

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