William Clifton1, Aaron Damon1, Christy Soares2, Eric Nottmeier1, Mark Pichelmann3. 1. Department of Neurological Surgery, Mayo Clinic Florida, Jacksonville, Florida, USA. 2. Florida State University College of Medicine, Tallahassee, Florida, USA. 3. Department of Neurosurgery, Mayo Clinic Health Systems, Eau Claire, Wisconsin, USA.
Abstract
INTRODUCTION: Three-dimensional (3D) printing of anatomical structures is a growing method of education for students and medical trainees. These models are generally produced as static representations of gross surface anatomy. In order to create a model that provides educators with a tool for demonstration of kinematic and physiologic concepts in addition to surface anatomy, a high-resolution segmentation and 3D-printingtechnique was investigated for the creation of a dynamic educational model. METHODS: An anonymized computed tomography scan of the cervical spine with a diagnosis of ossification of the posterior longitudinal ligament was acquired. Using a high-resolution thresholding technique, the individual facet and intervertebral spaces were separated, and models of the C3-7 vertebrae were 3D-printed. The models were placed on a myelography simulator and subjected to flexion and extension under fluoroscopy, and measurements of the spinal canal diameter were recorded and compared to in-vivo measurements. The flexible 3D-printed model was then compared to a static 3D-printed model to determine the educational benefit of demonstrating physiologic concepts. RESULTS: The canal diameter changes on the flexible 3D-printed model accurately reflected in-vivo measurements during dynamic positioning. The flexible model also was also more successful in teaching the physiologic concepts of spinal canal changes during flexion and extension than the static 3D-printed model to a cohort of learners. CONCLUSIONS: Dynamic 3D-printed models can provide educators with a cost-effective and novel educational tool for not just instruction of surface anatomy, but also physiologic concepts through 3D ex-vivo modeling of case-specific physiologic and pathologic conditions.
INTRODUCTION: Three-dimensional (3D) printing of anatomical structures is a growing method of education for students and medical trainees. These models are generally produced as static representations of gross surface anatomy. In order to create a model that provides educators with a tool for demonstration of kinematic and physiologic concepts in addition to surface anatomy, a high-resolution segmentation and 3D-printingtechnique was investigated for the creation of a dynamic educational model. METHODS: An anonymized computed tomography scan of the cervical spine with a diagnosis of ossification of the posterior longitudinal ligament was acquired. Using a high-resolution thresholding technique, the individual facet and intervertebral spaces were separated, and models of the C3-7 vertebrae were 3D-printed. The models were placed on a myelography simulator and subjected to flexion and extension under fluoroscopy, and measurements of the spinal canal diameter were recorded and compared to in-vivo measurements. The flexible 3D-printed model was then compared to a static 3D-printed model to determine the educational benefit of demonstrating physiologic concepts. RESULTS: The canal diameter changes on the flexible 3D-printed model accurately reflected in-vivo measurements during dynamic positioning. The flexible model also was also more successful in teaching the physiologic concepts of spinal canal changes during flexion and extension than the static 3D-printed model to a cohort of learners. CONCLUSIONS: Dynamic 3D-printed models can provide educators with a cost-effective and novel educational tool for not just instruction of surface anatomy, but also physiologic concepts through 3D ex-vivo modeling of case-specific physiologic and pathologic conditions.
Keywords:
3D printing; anatomy; cervical spine; medical education; medical simulation; neuroanatomy; ossification of the posterior longitudinal ligament; simulation; spine
Authors: Maohua Lin; Moaed A Abd; Alex Taing; Chi-Tay Tsai; Frank D Vrionis; Erik D Engeberg Journal: Sensors (Basel) Date: 2021-12-29 Impact factor: 3.576