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

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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.

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Data availability

All authors confirm the appropriateness of all dataset and software used for supporting the conclusion.

Code availability

Not applicable.

Data availability

Data are depicted in the tables.

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Acknowledgements

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Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Seyed Farzad Maroufi

  2. Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran

    Seyed Farzad Maroufi

  3. Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey

    Yücel Doğruel

  4. Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Ahmad Pour-Rashidi

  5. Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA

    Gurkirat S. Kohli

  6. School of Medicine, University of Louisville, Louisville, KY, USA

    Colson Tomberlin Parker

  7. Department of Neurosurgery, Stanford University, Palo Alto, CA, USA

    Tatsuya Uchida

  8. Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt

    Mohamed Z. Asfour

  9. Department of Neurosurgery, Hospital de Alta Complejidad en Red “El Cruce”, Florencio Varela, Buenos Aires, Argentina

    Clara Martin

  10. Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA

    Mariagrazia Nizzola

  11. Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Alessandro De Bonis

  12. Department of Neurosurgery, Suez Canal University, Ismailia, Egypt

    Mamdouh Tawfik-Helika

  13. Department of Pediatric Neurosurgery, Children’s Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Amin Tavallai

  14. Department of Neurosurgery, Indiana University, Indianapolis, IN, USA

    Aaron A. Cohen-Gadol

  15. Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, CA, 95817, USA

    Paolo Palmisciano

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  1. Seyed Farzad Maroufi
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  2. Yücel Doğruel
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  6. Tatsuya Uchida
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  8. Clara Martin
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  9. Mariagrazia Nizzola
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  10. Alessandro De Bonis
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  11. Mamdouh Tawfik-Helika
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  14. Paolo Palmisciano
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Contributions

All authors contributed to the study conception and design. The study was conceptualized and supervised by AAC-G. and PP Material preparation, data collection and analysis were performed by SFM, YD, AP-R, GSK, CP, TU, MZA, CM, MN, AD-B, MT-H, and AT. The first draft of the manuscript was written by SFM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Paolo Palmisciano.

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Maroufi, S.F., Doğruel, Y., Pour-Rashidi, A. et al. Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary 27, 91–128 (2024). https://doi.org/10.1007/s11102-023-01369-6

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