RESUMO
BACKGROUND: Augmented reality (AR) has become a promising tool in neurosurgery. It can minimise the anatomical challenges faced by conventional endoscopic or microscopic transsphenoidal reoperations and can assist in intraoperative guidance, preoperative planning, and surgical training. OBJECTIVES: The aims of this systematic review are to describe, compare, and evaluate the use of AR in endoscopic and microscopic transsphenoidal surgery, incorporating the latest primary research. METHODS: A systematic review was performed to explore and evaluate existing primary evidence for using AR in transsphenoidal surgery. A comprehensive search of MEDLINE and EMBASE was conducted from database inception to 11th August 2021 for primary data on the use of AR in microscopic and endoscopic endonasal skull base surgery. Additional articles were identified through searches on PubMed, Google Scholar, JSTOR, SCOPUS, Web of Science, Engineering Village, IEEE transactions, and HDAS. A synthesis without meta-analysis (SWiM) analysis was employed quantitatively and qualitatively on the impact of AR on landmark identification, intraoperative navigation, accuracy, time, surgeon experience, and patient outcomes. RESULTS: In this systematic review, 17 studies were included in the final analysis. The main findings were that AR provides a convincing improvement to landmark identification, intraoperative navigation, and surgeon experience in transsphenoidal surgery, with a further positive effect on accuracy and time. It did not demonstrate a convincing positive effect on patient outcomes. No studies reported comparative mortalities, morbidities, or cost-benefit indications. CONCLUSION: AR-guided transsphenoidal surgery, both endoscopic and microscopic, is associated with an overall improvement in the areas of intraoperative guidance and surgeon experience as compared with their conventional counterparts. However, literature on this area, particularly comparative data and evidence, is very limited. More studies with similar methodologies and quantitative outcomes are required to perform appropriate meta-analyses and to draw significant conclusions.