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BackgroundMedical 3D printing has brought the manufacturing world closer to the patient’s bedside than ever before. This requires hospitals and their personnel to update their quality assurance program to more appropriately accommodate the 3D printing fabrication process and the challenges that come along with it.ResultsIn this paper, we explored different methods for verifying the accuracy of a 3D printed anatomical model. Methods included physical measurements, digital photographic measurements, surface scanning, photogrammetry, and computed tomography (CT) scans. The details of each verification method, as well as their benefits and challenges, are discussed.ConclusionThere are multiple methods for model verification, each with benefits and drawbacks. The choice of which method to adopt into a quality assurance program is multifactorial and will depend on the type of 3D printed models being created, the training of personnel, and what resources are available within a 3D printed laboratory.

Three-dimensional (3D) printing is a method by which two-dimensional (2D) virtual data is converted to 3D objects by depositing various raw materials into successive layers. Even though the technology was invented almost 40 years ago, a rapid expansion in medical applications of 3D printing has only been observed in the last few years. 3D printing has been applied in almost every subspecialty of medicine for pre-surgical planning, production of patient-specific surgical devices, simulation, and training. While there are multiple review articles describing utilization of 3D printing in various disciplines, there is paucity of literature addressing applications of 3D printing in breast cancer management. Herein, we review the current applications of 3D printing in breast cancer management and discuss the potential impact on future practices.

The adoption of robotically assisted surgeries is increasing at a dramatic rate. The Da Vinci “was used in 80% of radical prostatectomies performed in the U.S. for 2008, just nine years after the system went on the market” [8]. The Da Vinci is but one of the systems driving the development of more versatile, more cost-effective and more autonomous systems. Robotic systems require real-time, accurate position information of the anatomy and surgical instruments to allow the surgical team to perform critical tasks. For example, Renishaw’s neuromate and Accuray’s CyberKnife both require the precise location of fiducial markers [9]. Others, like Cambridge Medical Robotics’ Versius and Medrobotics’ Flex operators rely upon active imaging or access to direct line of sight [10].

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