Methods: This was a prospective educational study with comparison with historical controls (reference cohort). At a temporal bone dissection course, eighteen participants performed structured self-assessment during three hours of VR simulation training of mastoidectomy before proceeding to cadaver dissection/surgery (intervention cohort). At a previous course, eighteen participants received similar VR simulation training but without the structured self-assessment (reference cohort). Final products from VR simulation and cadaveric dissection were video-recorded and assessed by two blinded raters using a 19-point modified Welling Scale.

Results: The intervention cohort completed fewer procedures (average 4.2) during VR simulation training than the reference cohort (average 5.7). Nevertheless, the intervention cohort achieved a significantly higher average dissection score both in VR simulation (11.1 points, 95% CI [10.6–11.5]) and subsequent cadaveric dissection (11.8 points, 95% CI [10.7–12.8]) compared with the reference cohort who scored 9.1 points (95% CI [8.7–9.5]) during VR simulation and 5.8 points (95% CI [4.8–6.8]) during cadaveric dissection.

Conclusion: Structured self-assessment is a valuable learning support during self-directed VR simulation training of mastoidectomy and the positive effect on performance transfers to subsequent cadaveric dissection performance.

Objective: Mastoidectomy is a cornerstone in the surgical management of middle and inner ear diseases. Unfortunately, training is challenged by insufficient access to human cadavers. Three-dimensional (3D) printing of temporal bones could alleviate this problem, but evidence on their educational effectiveness is lacking. It is largely unknown whether training on 3D-printed temporal bones improves mastoidectomy performance, including on cadavers, and how this training compares with virtual reality (VR) simulation. To address this knowledge gap, this study investigated whether training on 3D-printed temporal bones improves cadaveric dissection performance, and it compared this training with the already-established VR simulation.

Study design: Prospective cohort study of an educational intervention.

Setting: Tertiary university hospital, cadaver dissection laboratory, and simulation center in Copenhagen, Denmark.

Methods: Eighteen otorhinolaryngology residents (intervention) attending the national temporal bone dissection course received 3 hours of mastoidectomy training on 3D-printed temporal bones. Posttraining cadaver mastoidectomy performances were rated by 3 experts using a validated assessment tool and compared with those of 66 previous course participants (control) who had received time-equivalent VR training prior to dissection.

Results: The intervention cohort outperformed the controls during cadaver dissection by 29% (P < .001); their performances were largely similar across training modalities but remained at a modest level (~50% of the maximum score).

Conclusion: Mastoidectomy skills improved from training on 3D-printed temporal bone and seemingly more so than on time-equivalent VR simulation. Importantly, these skills transferred to cadaveric dissection. Training on 3D-printed temporal bones can effectively supplement cadaver training when learning mastoidectomy.

Keywords: 3D printing; additive manufacturing; education; mastoidectomy; neurotology; otology; rapid prototyping; surgical simulation; temporal bone; training.

Background: Hearing outcomes after CI surgery are dependent on electrode placement. CBCT is increasingly used for in-office temporal bone imaging and might be routinely used for pre- and post-surgical evaluation.

Methods: Thirty-six matched pairs of pre- and postimplant CBCT scans were obtained. These were registered with an atlas to model cochlear microstructures in each dataset. Electrode contact center points were automatically determined using thresholding and electrode insertion parameters were calculated. Automated localization and calculation were compared with manual segmentation of contact center points as well as manufacturer specifications.

Results: Automated electrode contact detection aligned with manufacturer specifications of spacing and our algorithms worked for both distantly- and closely spaced arrays. The average difference between the manual and the automated selection was 0.15 mm, corresponding to a 1.875 voxel difference in each plane at the scan resolution. For each case, we determined modiolar distance, angular insertion depth, and scalar location. These calculations also resulted in similar insertion values using manual and automated contact points as well as aligning with electrode properties.

Conclusion: Automated processing of implanted high-resolution CBCT images can provide the clinician with key information on electrode placement. This is one step toward routine use of clinical CBCT after CI surgery to inform and guide postoperative treatment.

Methods: De-identified, clinical computed tomography imaging of 9 pediatric patients aged 3 months to 12 years were obtained retrospectively. The patients represented normal anatomy and key structures were manually segmented using open source software. The OTOPLAN (CAScination AG, Bern, Switzerland) otological planning software was used for guided segmentation. An atlas-based algorithm was used for computerized, automated segmentation. Experience with the different approaches as well as time and resulting models were compared.

Results: Manual segmentation was time consuming but also the most flexible. The OTOPLAN software is not designed specifically for our purpose and therefore the number of structures that can be segmented is limited, there was some user-to-user variation as well as volume differences compared with manual segmentation. The atlas-based automated segmentation potentially allows a full range of structures to be segmented and produces segmentations comparable to those of manual segmentation with a processing time that is acceptable because of the minimal user interaction.

Conclusion: Segmentation is fundamental for patient-specific VR simulation for pre-surgical planning and rehearsal in temporal bone surgery. The automated segmentation algorithm currently offers the most flexible and feasible approach and should be implemented. Further research is needed in relation to cases of abnormal anatomy.

METHODS: In a randomized trial with a cross-over design, 24 students completed a total of four mastoidectomies in a VR temporal bone surgical simulator: two performances under UHF conditions using a digital microscope and two performances under conventional conditions using screen-based VR simulation. Performances were assessed by two blinded raters using an established assessment tool. In addition, CL was estimated as the relative change in secondary-task reaction time during simulation when compared with individual baseline measurements. Data were analyzed using linear mixed model analysis for repeated measurements.

RESULTS: The mean final-product performance score was significantly lower in UHF VR simulation compared to cVR simulation [mean difference 1.0 points out of 17 points, 95% CI (0.2–1.7), p = 0.02]. The most important factor for performance during UHF simulation was the ability to achieve stereovision (mean difference = 3.4 points, p < 0.001). Under the UHF VR condition, CL was significantly higher than during cVR (28% vs. 18%, respectively, p < 0.001).

CONCLUSION: UHF graphics in VR simulation training reduced performance and induced a higher CL in novices than conventional, screen-based VR simulation training. Consequently, UHF VR simulation training should be preceded by cVR training and might be better suited for the training of intermediates or experienced surgeons.

METHODS: A prospective, educational cohort study of a novice training program consisting of directed, self-regulated learning with distributed practice (5×3 procedures) in a virtual reality temporal bone simulator. The intervention consisted of structured self-assessment after each procedure using a rating form supported by small videos. Semi-structured telephone interviews upon completion of training were conducted with 13 out of 15 participants. Interviews were analysed using directed content analysis and triangulated with quantitative data on secondary task reaction time for cognitive load estimation and participants’ self-assessment scores.

RESULTS: Six major themes were identified in the interviews: goal-directed behaviour, use of learning supports for scaffolding of the training, cognitive engagement, motivation from self-assessment, self-assessment bias, and feedback on self-assessment (validation). Participants seemed to self-regulate their learning by forming individual sub-goals and strategies within the overall goal of the procedure. They scaffolded their learning through the available learning supports. Finally, structured self-assessment was reported to increase the participants’ cognitive engagement, which was further supported by a quantitative increase in cognitive load.

CONCLUSIONS: Structured self-assessment in simulation-based surgical training of mastoidectomy seems to promote cognitive engagement and motivation in the learning task and to facilitate self-regulated learning.

STUDY DESIGN: Prospective, controlled cohort study.

SETTING: Otorhinolaryngology (ORL) teaching hospitals and the Danish national TB course.

PARTICIPANTS: Thirty-eight ORL residents: 20 in the intervention cohort (decentralized training) and 18 in the control cohort (standard training during course).

INTERVENTION: Three months of access to decentralized VR simulation training at the local ORL department or the trainee’s home. A freeware VR simulator (the visible ear simulator [VES]) was used, supplemented by a range of learning supports for directed, self-regulated learning.

MAIN OUTCOME MEASURE: Mastoidectomy final-product scores from the VR simulations and cadaver dissection were rated using a modified Welling Scale by blinded expert raters.

RESULTS: Participants in the intervention cohort trained decentrally a median of 3.5 hours and performed significantly better than the control cohort during VR simulation (p < 0.01), which importantly also transferred to a 76% higher performance score during subsequent cadaver training (mean scores: 8.8 versus 5.0 points; p < 0.001).

CONCLUSIONS: Decentralized VR simulation training of mastoidectomy improves subsequent cadaver dissection performance and can potentially improve implementation of VR simulation surgical training.

METHODS: A panel of fellowship-trained content experts in mastoidectomy was surveyed in relation to the 16 items of the assessment tool to determine the skills needed for supervised and unsupervised surgery. We examined the consensus score to investigate the degree of agreement among respondents for each survey item as well as additional analyses to determine whether the reported skill level required for each survey item was significantly different for the supervised versus unsupervised level.

RESULTS: Ten panelists representing different US training programs responded. There was considerable consensus on cut-off scores for each item and trainee level between panelists, with moderate (0.62) to very high (0.95) consensus scores depending on assessment item. Further analyses demonstrated that the difference between supervised and unsupervised skill levels was significantly meaningful for all items. Finally, minimum-passing scores for each item was established.

CONCLUSION: We defined performance standards for the cross-institutional mastoidectomy assessment tool using the Angoff method. These cut-off scores that can be used to determine when trainees can progress from performance under supervision to performance without supervision. This can be used to guide training in a competency-based training curriculum.

METHODS: The performances of 11 experienced otosurgeons and 37 otorhinolaryngology residents. Participants performed three mastoidectomies in the Visible Ear Simulator. Nine residents contributed additional data on repeated practice in the simulator. One hundred and twenty-nine different performance metrics were collected by the simulator and final-product files were saved. These final products were analyzed using a modified Welling Scale by two blinded raters.

RESULTS: Seventeen metrics could discriminate between resident and experienced surgeons’ performances. These metrics mainly expressed various aspects of efficiency: Experts demonstrated more goal-directed behavior and less hesitancy, used less time, and selected large and sharp burrs more often. The combined metrics-based score (MBS) demonstrated significant discriminative ability between experienced surgeons and residents with a mean difference of 16.4% (95% confidence interval [12.6-20.2], P << 0.001). A pass/fail score of 83.6% was established. The MBS correlated poorly with the final-product score but excellently with the final-product score per time.

CONCLUSION: The MBS mainly reflected efficiency components of the mastoidectomy procedure, and although it could have some uses in self-directed training, it fails to measure and encourage safe routines. Supplemental approaches and feedback are therefore required in VR simulation training of mastoidectomy.