Background and objectives: Middle cerebral artery (MCA) intracranial aneurysms (IAs) present in different anatomic configurations and variations, making their precise preoperative study of paramount importance to tailor treatment. Surgical planning with a 3-dimensional interactive technology such as mixed reality (MxR) may have a relevant impact on anatomic understanding and surgical orientation. Methods: Twenty-five unruptured elective MCA IAs surgically prepared with MxR were prospectively included and matched with a cohort of 25 cases that were not prepared with MxR. Demographic, clinical, and aneurysm-related and surgery-related data were collected. Hologram preparation time (HPT) was recorded. The surgical preparation time, ie, from beginning of patient positioning to end of draping, and intraoperative time, ie, from skin incision to skin closure were compared between groups. MxR usability was evaluated through a standard Usefulness, Satisfaction, and Ease of use questionnaire assessing Usefulness, Ease of Use, Ease of Learning, and Satisfaction. Results: The mean HPT was 26 ± 10 minutes. In 2 cases (8%), surgical planning with MxR led to the modification of the approach or the head's positioning. The cohort prepared with MxR showed a lower mean surgical time from skin incision to skin closure (203 ± 64 minutes vs 229 ± 78 minutes, 95% CI -20, 58). The mean craniotomy diameter and mean surgical preparation time did not differ significantly. Mean scores for Usefulness, Ease of Use, Ease of Learning, and Satisfaction were equal or above 5 for 80% of the interviewed surgeons, and a subjective feeling of improved anatomic understanding after 3-dimensional visualization was reported by all interviewed neurosurgeons. Conclusion: The application of MxR on surgical planning of elective clipping of MCA IAs seemed to have relevant impact on surgical time and preoperative anatomic understanding while showing acceptable usability. HPT was significant and should be further reduced to be applicable broadly and preferably using automatic segmentation strategies.
Colombo et al. (Wed,) studied this question.