Action classification for endoscopic pituitary adenoma resection: a consensus-based study - Report - MDSpire

Action classification for endoscopic pituitary adenoma resection: a consensus-based study

  • By

  • Joachim Starup-Hansen

  • Danyal Z. Khan

  • Adrito Das

  • Joao Paulo Almeida

  • Sophia Bano

  • Anouk Borg

  • Kevin Cleary

  • Neil Dorward

  • Juan C. Fernandez-Miranda

  • Eduardo Torres-Rodríguez

  • Danail Stoyanov

  • Recai Yilmaz

  • Peter Weir

  • Daniel A. Donoho

  • Hani J. Marcus

  • April 11, 2026

  • 0 min

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Clinical Report: Consensus Study on Action Classification for Resection of Endoscopic Pituitary Adenomas

Overview

This study presents a consensus-based action classification ontology for the endoscopic transsphenoidal resection of pituitary adenomas. The framework aims to enhance surgical performance assessment and facilitate the development of AI models for improved surgical outcomes.

Background

Pituitary adenomas are prevalent brain tumors that can significantly affect patient quality of life and outcomes. The variability in surgical outcomes highlights the need for a standardized approach to assess surgical performance. Understanding the technical aspects of surgery through a detailed action classification can aid in training and improving surgical techniques.

Data Highlights

No numerical data or trial data was presented in the article.

Key Findings

  • The study developed a universal action classification ontology for endoscopic pituitary adenoma surgery.
  • AI models can analyze surgical actions to identify performance determinants linked to clinical outcomes.
  • Higher surgical skills in specific steps correlate with improved patient outcomes.
  • The framework facilitates standardized data curation for future AI model training.
  • Operative workflow analysis is essential for understanding surgical performance.

Clinical Implications

The action classification ontology can serve as a foundation for training surgical teams and improving surgical techniques. By leveraging AI for performance assessment, surgeons may enhance patient outcomes and reduce variability in surgical results.

Conclusion

This consensus study lays the groundwork for future research into AI applications in surgical performance assessment, potentially leading to improved outcomes in pituitary adenoma surgeries.

References

  1. Acta Neurochirurgica, 2026 -- Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis
  2. Frontiers in Surgery, 2026 -- Bilateral Approach Selection in Neuroendoscopic Surgery for Pituitary Adenomas and Health Economic Evaluation
  3. Transcavernous Endoscopic Technique for Treating Functional Pituitary Adenomas, 2024
  4. Effect of preoperative anemia on surgical outcomes in endonasal transsphenoidal surgery for pituitary adenoma: a matched-cohort study
  5. Pituitary incidentaloma: a Pituitary Society international consensus guideline statement, Nature Reviews Endocrinology, 2025
  6. WK PR on FPA Guidelines - Congress of Neurological Surgeons (CNS)
  7. European Society of Endocrinology Guidelines
  8. Comparison of endoscopic and endoscope-assisted microscopic transsphenoidal surgery for pituitary adenoma resection
  9. Safety and efficacy of endoscopic vs. microscopic approaches in pituitary adenoma surgery
  10. A core outcome set for pituitary surgery research: an international delphi consensus study
  11. Pituitary incidentaloma: a Pituitary Society international consensus guideline statement | Nature Reviews Endocrinology
  12. WK PR on FPA Guidelines - Congress of Neurological Surgeons (CNS)
  13. https://scholarworks.indianapolis.iu.edu/server/api/core/bitstreams/82bff87b-9ad5-4f4e-b667-cec5d3b4b0f7/content

Original Source(s)

Action classification for endoscopic pituitary adenoma resection: a consensus-based study

  • by Joachim Starup-Hansen, Danyal Z. Khan, Adrito Das, Joao Paulo Almeida, Sophia Bano, Anouk Borg, Kevin Cleary, Neil Dorward, Juan C. Fernandez-Miranda, Eduardo Torres-Rodríguez, Danail Stoyanov, Recai Yilmaz, Peter Weir, Daniel A. Donoho, Hani J. Marcus

  • April 11, 2026

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