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Neurosurgical skills analysis by machine learning models: systematic review.
Oleg Titov1,2, Andrey Bykanov3, David Pitskhelauri3
1Burdenko Neurosurgery Center, Moscow, Russia. oleg96titov@mail.ru.
Neurosurgical Review
|May 16, 2023
Summary
Machine learning (ML) models are revolutionizing neurosurgical training by accurately assessing surgical skills. These AI systems outperform human experts in tasks like skill classification and instrument recognition, enhancing medical education.
Area of Science:
- Neurosurgery and Medical Education
- Artificial Intelligence in Healthcare
- Surgical Skill Assessment
Background:
- Machine learning (ML) is increasingly integrated into medical applications, including neurosurgery.
- Assessing and improving neurosurgical skills is critical for patient outcomes.
- A systematic review is needed to summarize ML applications in neurosurgical skill analysis.
Approach:
- Conducted a systematic review following PRISMA guidelines, searching PubMed and Google Scholar until November 2022.
- Assessed study quality using the Medical Education Research Study Quality Instrument (MERSQI).
- Included 17 studies focusing on oncological, spinal, and vascular neurosurgery using microsurgical and endoscopic techniques.
Key Points:
- ML models analyzed tasks like tumor resection, discectomy, and vessel dissection using VR simulators and video data.
- ML applications included skill level classification, expert-novice comparison, instrument recognition, and blood loss prediction.
- Machine learning algorithms, such as support vector machines and k-nearest neighbors, achieved over 90% accuracy in skill classification.
Conclusions:
- Machine learning models effectively perform neurosurgical skill classification, object detection, and outcome prediction.
- ML models demonstrated superior performance compared to human experts in evaluated tasks.
- Further research is warranted to explore ML's potential across diverse neurosurgical subspecialties and skills.
