Current Status of Artificial Intelligence Technologies in Pituitary Adenoma Surgery: A Scoping Review Publisher Pubmed



Maroufi SF^{1, 2} ; Dogruel Y³ ; Pourrashidi A⁴ ; Kohli GS⁵ ; Parker CT⁶ ; Uchida T⁷ ; Asfour MZ⁸ ; Martin C⁹ ; Nizzola M¹⁰ ; De Bonis A¹¹ ; Tawfikhelika M¹² ; Tavallai A¹³ ; Cohengadol AA¹⁴ ; Palmisciano P¹⁵
Source: Pituitary Published:2024

Abstract

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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.