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Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery
Shima Shahjouei1,2, Seyed Mohammad Ghodsi2, Morteza Zangeneh Soroush3,4
1Neurology Department, Neuroscience Institute, Geisinger Health System, Danville, PA 17822, USA.
Journal of Clinical Medicine
|April 30, 2021
Summary
An artificial neural network (ANN) predicts safe temporary artery occlusion (TAO) times during aneurysm surgery. This tool enhances patient safety by estimating optimal clipping durations, reducing risks of cerebral ischemia.
Area of Science:
- Neurosurgery
- Artificial Intelligence
- Medical Imaging
Background:
- Temporary artery clipping is crucial for cerebral aneurysm management but carries a risk of cerebral ischemia.
- Developing predictive models for safe temporary artery occlusion (TAO) is essential to mitigate surgical risks.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) model for predicting the safe clipping time during temporary artery occlusion (TAO) in intracranial aneurysm surgery.
Main Methods:
- A three-layer artificial neural network was designed.
- Input variables included patient age, cerebral artery diameters (MCA, ACA), blood flow velocity, and Fisher grading scale from CT scans.
- The model was trained on 105 patients and validated on 20 patients.
Main Results:
- The ANN model predicted an overall safe clipping time of up to 960 seconds for TAO.
- Key predictors for safe TAO duration were mean blood flow velocity in the left middle cerebral artery (MCA) and anterior cerebral artery (ACA), and the Fisher grading scale.
Conclusions:
- The developed ANN provides an auxiliary framework to enhance the accuracy of estimated safe TAO duration.
- This predictive tool can improve the safety and outcomes of intracranial aneurysm surgery.
