Morphological validation of a novel bi-material 3D-printed model of temporal bone for middle ear surgery education

Abstract

Background: A new model of 3D-printed temporal bone with an innovative distinction between soft and hard tissues is described and presented in the present study. An original method is reported to quantify the model's ability to reproduce the complex anatomy of this region.Methods: A CT-scan of temporal bone was segmented and prepared to obtain 3D files adapted to multimaterial printing technique. A final product was obtained with two different resins differentiating hard from soft tissues. The reliability of the anatomy was evaluated by comparing the original CT-scan and the preprocessed files sent to the printer in a first step, and by quantifying the printing technique in a second step.Firstly, we evaluated the segmentation and mesh correction steps by segmenting each anatomical region in the CT-scan by two different other operators without mesh corrections, and by computing distances between the obtained geometries and the pre-processed ones. Secondly, we evaluated the printing technique by comparing the printed geometry imaged using µCT with the pre-processed one.Results: The evaluation of the segmentation and mesh correction steps revealed that the distance between both geometries was globally less that one millimeter for each anatomical region and close to zero for regions such as temporal bone, semicircular canals or facial nerve. The evaluation of the printing technique revealed mismatches of 0.045±0.424 mm for soft and −0.093±0.240 mm for hard tissues between the initial prepared geometry and the actual printed model. Conclusions:While other reported models for temporal bone are simpler and have only been validated subjectively, we objectively demonstrated in the present study that our novel artificial bi-material temporal bone is consistent with the anatomy and thus could be considered into ENT surgical education programs.The methodology used in this study is quantitative, inspired by engineer sciences, making it the first of its kind.The validity of the manufacturing process has also been verified and could, therefore, be extended to other specialties, emphasizing the importance of cross-disciplinary collaborations concerning new technologies.

3D‐Printed Models for Temporal Bone Surgical Training: A Systematic Review

“…This was done either by CT‐scanning the 3D‐printed model and comparing its dimensions with a CT of the cadaver or by manually measuring cadavers and 3D‐printed models during dissection. Most of these studies combined quantitative measurements with qualitative evaluation/description of the model by experts, 27,45–47 with only a single study combining quantitative measurements with a structured evaluation using a questionnaire 30 . In general, models accurately replicated larger surfaces and bony parts; contrarily, small and important structures (eg, the ossicles) were hard to replicate 22 , 23 …”

Preoperative simulation using three‐dimensional printer in four temporal bone surgeries

“… 13 Additionally, it is useful not only in the simulation of surgery but also in surgery education. 14 In our department, practice courses using actual temporal bones are sometimes held, but the opportunities are limited. In surgical education, drilling the original model using a three‐dimensional printer is important, 15 but there is a difference in drilling simulated bone from real temporal bone.…”

“… 19 Additionally, by creating full‐scale models made from the material to be drilled, we can expect higher‐quality preoperative simulations. 14 …”

A flexible design framework to design graded porous bone scaffolds with adjustable anisotropic properties

“…The external geometry was then extracted from thresholding the µCT volume, and the printed support was removed using standard Boolean operations and mesh correction. Geometries extracted from µCT were compared to the initial geometry issued from CAD by computing pointto-surface distances as described in (Chauvelot et al, 2020), based on the Python library PyVista. Using the CAD geometry as a reference, the two surface meshes were initially put in the same reference frame so as to minimize the mean point-to-surface distance between both geometries, and point-to-surface distances were then calculated and mapped onto the reference CAD geometry.…”