Conventional navigation systems (CNS) in surgery require strong spatial cognitive abilities and hand-eye coordination. Augmented Reality Navigation Systems (ARNS) provide 3D guidance and may overcome these challenges, but their accuracy and efficiency compared to CNS have not been systematically evaluated. In this randomized crossover study with 36 participants from different professional backgrounds (surgeons, students, engineers), drilling accuracy, time and perceived workload were evaluated using ARNS and CNS. For the first time, this study provides compelling evidence that ARNS and CNS have comparable accuracy in translational error. Differences in angle and depth error with ARNS were likely due to limited stereoscopic vision, hardware limitations, and design. Despite this, ARNS was preferred by most participants, including surgeons with prior navigation experience, and demonstrated a significantly better overall user experience. Depending on accuracy requirements, ARNS could serve as a viable alternative to CNS for guided drilling, with potential for future optimization.

Background: Augmented reality head-mounted displays could overcome the spatial dissociation between medical imaging and the surgical field, which may be particularly important in anatomically dense regions, such as the head and neck. Although many head-mounted displays offer markerless inside-out tracking at a fraction of the cost of navigation systems, their overlay accuracy with superimposition (SI) modality onto the surgical field remains limited. The virtual twin (VT), displaying holography adjacent to the surgical field, may offer a viable alternative. However, its performance is still unclear. Objective: This study aimed to compare the accuracy and efficiency of the two visualization modalities, SI and VT, for anatomical localization in the head and neck region. Methods: In a randomized crossover trial to compare two augmented reality visualization modalities (SI and VT), 38 participants used a HoloLens 2 to localize point, line-based, and volume-based anatomical structures on head phantoms. Their performance was evaluated with respect to accuracy, workload, time, and user experience. Results: SI achieved significantly better point localization accuracy than VT both in absolute (mean 14.4, SD 4.2 mm vs mean 15.8, SD 5.5 mm; P =.003) and relative accuracy (mean 3.4, SD 2.2 mm vs mean 6.0, SD 5.0 mm; P <.001). In line-based structures, accuracy was comparable between SI (average surface distance [ASD], mean 23.4, SD 4.1 mm; Hausdorff distance [HD], mean 31.5, SD 7.8 mm) and VT (ASD=mean 23.0, SD 4.5 mm; P =.51; HD=mean 31.0, SD 7.5 mm; P =.57). However, SI showed significantly higher deviation than VT in volume-based structure (ASD=mean 37.1, SD 13.8 mm vs mean 34.1, SD 14.2 mm; P =.01; HD=mean 52.0, SD 16.8 mm vs mean 49.1, SD 15.8 mm; P =.03). Participants were faster with SI ( P =.02), while workload NASA-TLX (National Aeronautics and Space Administration Task Load Index) scores did not demonstrate a significant difference ( P= .79). Conclusions: Given that SI did not clearly outperform VT under overlaid soft tissue and viewing challenges, VT remains a viable alternative in certain surgical scenarios where high accuracy is not required. Future research should focus on optimizing viewing angle guidance and the linkage between the anatomical target and the skin surface.

The current gold standard of computer-assisted jaw reconstruction includes raising microvascular bone flaps with patient-specific 3D-printed cutting guides. The downsides of cutting guides are invasive fixation, periosteal denudation, preoperative lead time and missing intraoperative flexibility. This study aimed to investigate the feasibility and accuracy of a robot-assisted cutting method for raising iliac crest flaps compared to a conventional 3D-printed cutting guide. In a randomized crossover design, 40 participants raised flaps on pelvic models using conventional cutting guides and a robot-assisted cutting method. The accuracy was measured and compared regarding osteotomy angle deviation, Hausdorff Distance (HD) and Average Hausdorff Distance (AVD). Duration, workload and usability were further evaluated. The mean angular deviation for the robot-assisted cutting method was 1.9±1.1° (mean±sd) and for the 3D-printed cutting guide it was 4.7±2.9° (p<0.001). The HD resulted in a mean value of 1.5±0.6mm (robot) and 2.0±0.9mm (conventional) (p<0.001). For the AVD, this was 0.8±0.5mm (robot) and 0.8±0.4mm (conventional) (p=0.320). Collaborative robot-assisted cutting is an alternative to 3D-printed cutting guides in experimental static settings, achieving slot design benefits with less invasiveness and higher intraoperative flexibility. In the next step, the results should be tested in a dynamic environment with a moving phantom and on the cadaver.

Although additive manufacturing enables cost-effective surgical training models, variation in materials and printing parameters limits standardization. This study combined biomechanical testing and user evaluation to identify fused deposition modeling (FDM) settings for osteotomy simulation. Porcine ribs and three-dimensional (3D)-printed models were compared using three-point bending tests. Subsequently, 32 participants performed osteotomies using an ultrasonic device, a Lindemann bur, and a reciprocating saw. Haptic feedback was assessed by questionnaire, while surface artifacts were evaluated by two blinded evaluators. With 25% gyroid infill and two outer layers, maximum bending forces ranged from 322.9 to 552.1 N across the printed materials, compared with 604.9 N for the porcine ribs, with polylactic acid (PLA) showing the closest approximation. Polycarbonate (PC) achieved the highest haptic rating (6.7 ± 1.8), followed by acrylonitrile styrene acrylate (ASA), polyethylene terephthalate glycol (PETG), and PLA, with a significant difference between PC and PLA (p = 0.029). The reciprocating saw received the highest ratings across all criteria. Surface artifacts differed between materials (p < 0.001), with PETG and PLA showing more melting and fraying, and ASA and PC more stringing. No significant differences were observed between students and doctors. Overall, PLA most closely approximated the reference maximum bending force, whereas PC and ASA achieved the highest perceived tactile realism.

Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare.