Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.

PURPOSE: Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations. METHODS: PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies. RESULTS: Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately. CONCLUSION: AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.

Understanding Anatomy of the Petrous Pyramid-A New Compartmental Approach.

BACKGROUND: Learning surgical anatomy of the petrous pyramid can be a challenge, especially in the beginning of the training process. Providing an easier, holistic approach can be of help to everyone with interest in learning and teaching skull base anatomy. We present the complex organization of petrous pyramid anatomy using a new compartmental approach that is simple to understand and remember. METHODS: The surfaces of the petrous pyramid of two temporal bones were examined; and the contents of the petrous pyramid of 8 temporal bones were exposed through progressive drilling of the superior surface. RESULTS: The petrous pyramid is made up of a bony container, and its contents were grouped into 4 compartments (mucosal, cutaneous, neural, and vascular). Two reference lines were identified (mucosal and external-internal auditory canal lines) intersecting at the level of the middle ear. The localization of contents relative to these reference lines was then described, and 2 methods of segmentation (the X method and the V method) were then proposed. This description was then used to describe middle ear relationships, facial nerve anatomy, and air cell distribution. CONCLUSIONS: This new compartmental approach allows a comprehensive understanding of the distribution of petrous pyramid contents. Dividing it into anatomic compartments, and then navigating this mental map along specific reference points, lines, spaces, and segments, could create a useful tool to teach or learn its complex tridimensional anatomy.