Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review
Authors' 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.
Study context
- Population
- Studies on artificial intelligence (AI)/machine learning (ML) in pituitary adenoma surgery (PubMed, Embase, Web of Science, Scopus)44 studies
- Outcome
- Current status of AI/ML in pituitary adenoma surgery
Methodological detailsPRISMA-ScR guidelines
- PRISMA-ScR guidelines
Main finding
As reported in the abstract
- Articles identified
- 2438
- Studies meeting inclusion criteria
- 44
- Different algorithms used across studies
- 17
- Main predicted outcome variables: outcome (remission, recurrence or progression, gross-total resection, vision improvement, hormonal recovery), complications (CSF leak, readmission, hyponatremia, hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, Ki-67 labeling)
- 4
- Studies on workflow analysis and real-time navigation, discussed separately
- 3
Original publication
Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary. Published 6 January 2024. doi:10.
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