Quantitative Assessment of 3D Printed Blood Vessels Produced with J750™ Digital Anatomy™ for Suture Simulation

Blood vessels anastomosis is one of the most challenging and delicate tasks to learn in many surgical specialties, especially for vascular and abdominal surgeons. Such a critical skill implies a learning curve that goes beyond technical execution. The surgeon needs to gain proficiency in adapting gestures and the amount of force expressed according to the type of tissue he/she is dealing with. In this context, surgical simulation is gaining a pivotal role in the training of surgeons, but currently available simulators can provide only standard or simplified anatomies, without the chance of presenting specific pathological conditions and rare cases. 3D printing technology, allowing the manufacturing of extremely complex geometries, find a perfect application in the production of realistic replica of patient-specific anatomies. According to available technologies and materials, morphological aspects can be easily handled, while the reproduction of tissues mechanical properties still poses major problems, especially when dealing with soft tissues. The present work focuses on blood vessels, with the aim of identifying – by means of both qualitative and quantitative tests – materials combinations able to best mimic the behavior of the biological tissue during anastomoses, by means of J750™ Digital Anatomy™ technology and commercial photopolymers from Stratasys. Puncture tests and stitch traction tests are used to quantify the performance of the various formulations. Surgical simulations involving anastomoses are performed on selected clinical cases by surgeons to validate the results. A total of 37 experimental materials were tested and 2 formulations were identified as the most promising solutions to be used for anastomoses simulation. Clinical applicative tests, specifically selected to challenge the new materials, raised additional issues on the performance of the materials to be considered for future developments.

Digital Anatomy: Opportunities and Challenges for Medical Sciences (Preprint)

UNSTRUCTURED Anatomy has been the cornerstone of medical education for centuries. However, given advances in the Internet of Things, this landscape has been augmented in the past decade, shifting towards a greater focus on adopting digital technologies. Indeed, digital Anatomy is emerging as a new discipline and represents an opportunity to embrace advances in digital health technologies and apply them to the domain of modern medical sciences. This is not only a result of a multidisciplinary exercise but an active response to the change of medical education landscape and the rapid development of medical technology. Notably, the use of augmented and virtual reality as well as mobile and platforms and 3D printing in modern anatomy has dramatically increased in the last 5 years. It has not only revolutionized undergraduate anatomy education but is shifting the paradigm of pre- and vocational training for medical professionals, advancing healthcare. This review outlines the emerging area of digital anatomy and summarises recent practice-changing studies in medical science education and research. Importantly, we present a SWOT analysis of the opportunities and challenges for incorporating digital anatomy, discussing both the strength and weakness and the underlying threats and opportunities for educators, researchers, and the new generation of health professionals. In so doing this review will serve to identify an important role for digital anatomy to play in both the learning and teaching of medicine and health sciences as well as its practice, prompting new questions for future investigations.

Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology

In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system.

Comparing cost and print time estimates for six commercially-available 3D printers obtained through slicing software for clinically relevant anatomical models

Background 3D printed patient-specific anatomical models have been applied clinically to orthopaedic care for surgical planning and patient education. The estimated cost and print time per model for 3D printers have not yet been compared with clinically representative models across multiple printing technologies. This study investigates six commercially-available 3D printers: Prusa i3 MK3S, Formlabs Form 2, Formlabs Form 3, LulzBot TAZ 6, Stratasys F370, and Stratasys J750 Digital Anatomy. Methods Seven representative orthopaedic standard tessellation models derived from CT scans were imported into the respective slicing software for each 3D printer. For each printer and corresponding print setting, the slicing software provides a print time and material use estimate. Material quantity was used to calculate estimated model cost. Print settings investigated were infill percentage, layer height, and model orientation on the print bed. The slicing software investigated are Cura LulzBot Edition 3.6.20, GrabCAD Print 1.43, PreForm 3.4.6, and PrusaSlicer 2.2.0. Results The effect of changing infill between 15% and 20% on estimated print time and material use was negligible. Orientation of the model has considerable impact on time and cost with worst-case differences being as much as 39.30% added print time and 34.56% added costs. Averaged across all investigated settings, horizontal model orientation on the print bed minimizes estimated print time for all 3D printers, while vertical model orientation minimizes cost with the exception of Stratasys J750 Digital Anatomy, in which horizontal orientation also minimized cost. Decreasing layer height for all investigated printers increased estimated print time and decreased estimated cost with the exception of Stratasys F370, in which cost increased. The difference in material cost was two orders of magnitude between the least and most-expensive printers. The difference in build rate (cm3/min) was one order of magnitude between the fastest and slowest printers. Conclusions All investigated 3D printers in this study have the potential for clinical utility. Print time and print cost are dependent on orientation of anatomy and the printers and settings selected. Cost-effective clinical 3D printing of anatomic models should consider an appropriate printer for the complexity of the anatomy and the experience of the printer technicians.