Article: 3D-printed cranial models simulating operative field depth for microvascular training in neurosurgery.
Surgical neurology international
2021 Volume 12, Page(s) 213
Abstract: Background: The skills required for neurosurgical operations using microsurgical techniques in a deep operating field are difficult to master in the operating room without risk to patients. Although there are many microsurgical training models, most do ... ...
| Abstract | Background: The skills required for neurosurgical operations using microsurgical techniques in a deep operating field are difficult to master in the operating room without risk to patients. Although there are many microsurgical training models, most do not use a skull model to simulate a deep field. To solve this problem, 3D models were created to provide increased training in the laboratory before the operating room, improving patient safety. Methods: A patient's head was scanned using computed tomography. The data were reconstructed and converted into a standard 3D printing file. The skull was printed with several openings to simulate common surgical approaches. These models were then used to create a deep operating field while practicing on a chicken thigh (femoral artery anastomosis) and on a rat (abdominal aortic anastomosis). Results: The advantages of practicing with the 3D printed models were clearly demonstrated by our trainees, including appropriate hand position on the skull, becoming comfortable with the depth of the anastomosis, and simulating proper skull angle and rigid fixation. One limitation is the absence of intracranial structures, which is being explored in future work. Conclusion: This neurosurgical model can improve microsurgery training by recapitulating the depth of a real operating field. Improved training can lead to increased accuracy and efficiency of surgical procedures, thereby minimizing the risk to patients. |
|---|---|
| Language | English |
| Publishing date | 2021-05-10 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 2229-5097 |
| ISSN | 2229-5097 |
| DOI | 10.25259/SNI_849_2020 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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