Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery:
Victor E Staartjes1,2, Martina Sebök1, Patricia G Blum1
1Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
Acta Neurochirurgica
|May 3, 2020
Summary
Machine learning (ML) models can predict outcomes for unruptured intracranial aneurysms (UIAs) microsurgery. This pilot study shows ML
Area of Science:
- Neurosurgery
- Medical Informatics
- Data Science
Background:
- The treatment of unruptured intracranial aneurysms (UIAs) involves balancing risks and scores.
- Machine learning (ML) has shown promise in predicting surgical outcomes for other neurosurgical procedures.
Purpose of the Study:
- To evaluate the feasibility of ML-based prediction for clinical endpoints in the microsurgical management of UIAs.
Main Methods:
- Developed and internally validated ML models using data from a prospective registry.
- Included variables such as patient demographics, aneurysm characteristics, and treatment details.
- Predicted neurological outcome, new deficits, and complications at discharge using modified Rankin scale (mRS) and Clavien-Dindo grading (CDG).
Main Results:
- 156 patients with UIAs were included in the study.
- Poor neurological outcome (mRS ≥ 3) occurred in 7.7% of patients.
- The ML models achieved area under the curve (AUC) values of 0.63-0.77 and accuracy of 0.78-0.91.
Conclusions:
- ML application enables prediction of early clinical endpoints following UIA microsurgery.
- This pilot study provides a foundation for developing an externally validated multicenter clinical prediction model.
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