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

Developing a production workflow for 3D-printed temporal bone surgical simulators

INTRODUCTION

3D-printed temporal bone models enable the training and rehearsal of complex otological procedures. To date, there has been no consolidation of the literature regarding the developmental process of 3D-printed temporal bone models. A brief review of the current literature shows that many of the key surgical landmarks of the temporal bone are poorly represented in models. This study aims to propose a novel design and production workflow to produce high-fidelity 3D-printed temporal bone models for surgical simulation.

METHODS

Developmental phases for data extraction, 3D segmentation and Computer Aided Design (CAD), and fabrication are outlined. The design and fabrication considerations for key anatomical regions, such as the mastoid air cells and course of the facial nerve, are expounded on with the associated strategy and design methods employed. To validate the model, radiological measurements were compared and a senior otolaryngologist performed various surgical procedures on the model.

RESULTS

Measurements between the original scans and scans of the model demonstrate sub-millimetre accuracy of the model. Assessment by the senior otologist found that the model was satisfactory in simulating multiple surgical procedures.

CONCLUSION

This study offers a systematic method for creating accurate 3D-printed temporal bone models for surgical training. Results show high accuracy and effectiveness in simulating surgical procedures, promising improved training and patient outcomes.

Clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: neurosurgical and otolaryngologic conditions

BACKGROUND

Medical three dimensional (3D) printing is performed for neurosurgical and otolaryngologic conditions, but without evidence-based guidance on clinical appropriateness. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides appropriateness recommendations for neurologic 3D printing conditions.

METHODS

A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with neurologic and otolaryngologic conditions. Each study was vetted by the authors and strength of evidence was assessed according to published guidelines.

RESULTS

Evidence-based recommendations for when 3D printing is appropriate are provided for diseases of the calvaria and skull base, brain tumors and cerebrovascular disease. Recommendations are provided in accordance with strength of evidence of publications corresponding to each neurologic condition combined with expert opinion from members of the 3D printing SIG.

CONCLUSIONS

This consensus guidance document, created by the members of the 3D printing SIG, provides a reference for clinical standards of 3D printing for neurologic conditions.

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

Preoperative simulation using a three-dimensional printer is effective to perform safe surgery by knowing the range limit of drilling in the temporal bone. Moreover, simulations using models are thought to be useful for education of young surgeon.

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

Since the success of bone regenerative medicine depends on scaffold morphological and mechanical properties, numerous scaffolds designs have been proposed in the last decade, including graded structures that are suited to enhance tissue ingrowth. Most of these structures are based either on foams with a random pore definition, or on the periodic repetition of a unit cell (UC). These approaches are limited by the range of target porosities and obtained effective mechanical properties, and do not permit to easily generate a pore size gradient from the core to the periphery of the scaffold. In opposition, the objective of the present contribution is to propose a flexible design framework to generate various three-dimensional (3D) scaffolds structures including cylindrical graded scaffolds from the definition of a UC by making use of a non-periodic mapping. Conformal mappings are firstly used to generate graded circular cross-sections, while 3D structures are then obtained by stacking the cross-sections with or without a twist between different scaffold layers. The effective mechanical properties of different scaffold configurations are presented and compared using an energy-based efficient numerical method, pointing out the versatility of the design procedure to separately govern longitudinal and transverse anisotropic scaffold properties. Among these configurations, a helical structure exhibiting couplings between transverse and longitudinal properties is proposed and permits to extend the adaptability of the proposed framework. In order to investigate the capacity of common additive manufacturing techniques to fabricate the proposed structures, a subset of these configurations is elaborated using a standard SLA setup, and subjected to experimental mechanical testing. Despite observed geometric differences between the initial design and the actual obtained structures, the effective properties are satisfyingly predicted by the proposed computational method. Promising perspectives are offered concerning the design of self-fitting scaffolds with on-demand properties depending on the clinical application.

Deep learning-based approach for the automatic segmentation of adult and pediatric temporal bone computed tomography images

Background

Automatic segmentation of temporal bone computed tomography (CT) images is fundamental to image-guided otologic surgery and the intelligent analysis of CT images in the field of otology. This study was conducted to test a convolutional neural network (CNN) model that can automatically segment almost all temporal bone anatomy structures in adult and pediatric CT images.

Methods

A dataset comprising 80 annotated CT volumes was collected, of which 40 samples were obtained from adults and 40 from children. A further 60 annotated CT volumes (30 from adults and 30 from children) were used to train the model. The remaining 20 annotated CT volumes were employed to determine the model's generalizability for automatic segmentation. Finally, the Dice coefficient (DC) and average symmetric surface distance (ASSD) were utilized as metrics to evaluate the performance of the CNN model. Two independent-sample t-tests were used to compare the test set results of adults and children.

Results

In the adult test set, the mean DC values of all the structures ranged from 0.714 to 0.912, and the ASSD values were less than 0.24 mm for 11 structures. In the pediatric test set, the mean DC values of all the structures ranged from 0.658 to 0.915, and the ASSD values were less than 0.18 mm for 11 structures. There was no statistically significant difference between the adult and child test sets in most temporal bone structures.

Conclusions

Our CNN model shows excellent automatic segmentation performance and good generalizability for both adult and pediatric temporal bone CT images, which can help to advance otologist education, intelligent imaging diagnosis, surgery simulation, application of augmented reality, and preoperative planning for image-guided otology surgery.

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

OBJECTIVE

3D-printed models hold great potential for temporal bone surgical training as a supplement to cadaveric dissection. Nevertheless, critical knowledge on manufacturing remains scattered, and little is known about whether use of these models improves surgical performance. This systematic review aims to explore (1) methods used for manufacturing and (2) how educational evidence supports using 3D-printed temporal bone models.

DATA SOURCES

PubMed, Embase, the Cochrane Library, and Web of Science.

REVIEW METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, relevant studies were identified and data on manufacturing and validation and/or training extracted by 2 reviewers. Quality assessment was performed using the Medical Education Research Study Quality Instrument tool; educational outcomes were determined according to Kirkpatrick's model.

RESULTS

The search yielded 595 studies; 36 studies were found eligible and included for analysis. The described 3D-printed models were based on computed tomography scans from patients or cadavers. Processing included manual segmentation of key structures such as the facial nerve; postprocessing, for example, consisted of removal of print material inside the model. Overall, educational quality was low, and most studies evaluated their models using only expert and/or trainee opinion (ie, Kirkpatrick level 1). Most studies reported positive attitudes toward the models and their potential for training.

CONCLUSION

Manufacturing and use of 3D-printed temporal bones for surgical training are widely reported in the literature. However, evidence to support their use and knowledge about both manufacturing and the effects on subsequent surgical performance are currently lacking. Therefore, stronger educational evidence and manufacturing knowhow are needed for widespread implementation of 3D-printed temporal bones in surgical curricula.