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



Maroufi SF1, 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
Authors
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Authors Affiliations
  1. 1. Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
  3. 3. Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey
  4. 4. Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
  6. 6. School of Medicine, University of Louisville, Louisville, KY, United States
  7. 7. Department of Neurosurgery, Stanford University, Palo Alto, CA, United States
  8. 8. Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt
  9. 9. Department of Neurosurgery, Hospital de Alta Complejidad en Red “El Cruce�, Buenos Aires, Florencio Varela, Argentina
  10. 10. Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
  11. 11. Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
  12. 12. Department of Neurosurgery, Suez Canal University, Ismailia, Egypt
  13. 13. Department of Pediatric Neurosurgery, Children’s Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran
  14. 14. Department of Neurosurgery, Indiana University, Indianapolis, IN, United States
  15. 15. Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, 95817, CA, United States

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.
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