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12 The ANNs and logistic models predicted shunt infection with an accuracy of 83.1% and 55.7%, respectively. compared artificial neural networks (ANNs) and traditional logistic regression models for predicting ventriculoperitoneal shunt infections in childhood hydrocephalus. 3įrom literature reviews we find evidence that ML has been applied in predicting neurosurgical complications, particularly SSI.

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demonstrated that ML algorithms such as NB, logistic regression, and k-nearest neighbors (k-NN) may be powerful methods of selecting surgical candidates who have a high likelihood of remaining free from seizures in temporal lobe epilepsy. reported that the multimodality of ML, including linear discriminant analysis, naive Bayes (NB), and support vector machines, has high accuracy in surgical outcome prediction in patients with mesial temporal lobe epilepsy. Several ML algorithms have been developed using complex mathematical models that can learn from clinical data from, for example, neuro-oncology, neurovascular surgery, traumatic brain injury, and epilepsy. Machine learning (ML) is used for outcome prediction in the neurosurgical field.

The financial burden caused by craniotomy infections is often compounded by the direct costs incurred by prolonged hospitalization of the patient, diagnostic tests, treatment, and reoperation. conducted a cost analysis of craniotomy infections, identifying an estimated cost per case of infection at £9283. Such complications can impact morbidity, mortality, and economics. S urgical site infection (SSI) following neurosurgical operations is a burdensome complication in the field.