A systematic review on machine learning in neurosurgery: the future of decision making in patient care.

Abstract

Current practice of neurosurgery depends on clinical practice guidelines and evidence-based research publications that derive results using statistical methods. However, statistical analysis methods have some limitations such as the inability to analyze nonlinear variables, requiring setting a level of significance, being impractical for analyzing large amounts of data and the possibility of human bias. Machine learning is an emerging method for analyzing massive amounts of complex data which relies on algorithms that allow computers to learn and make accurate predictions. During the past decade, machine learning has been increasingly implemented in medical research as well as neurosurgical publications. This systematical review aimed to assemble the current neurosurgical literature that machine learning has been utilized, and to inform neurosurgeons on this novel method of data analysis.

Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

“…The increase of machine learning algorithms, and deep learning, enables the use of high-dimensional data, such as free text and imaging, to improve the accuracy and performance of prediction models. The increasing use of machine learning for the analysis of unstructured, high-dimensional data parallels the current trends in predictive modeling in medicine [44,45]. Neurosurgical examples include machine learning algorithms for glioblastoma, deep brain stimulation, traumatic brain injury, stroke, and spine surgery [38,[44][45][46][47][48][49][50][51][52][53].…”

“…The increasing use of machine learning for the analysis of unstructured, high-dimensional data parallels the current trends in predictive modeling in medicine [44,45]. Neurosurgical examples include machine learning algorithms for glioblastoma, deep brain stimulation, traumatic brain injury, stroke, and spine surgery [38,[44][45][46][47][48][49][50][51][52][53]. Deep learning algorithms are also increasingly being used to further improve the WHO 2016 classification of high-grade gliomas via histological and biomolecular variables for more concise diagnosis and classification of gliomas [54][55][56].…”

Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning

“…This study indicated that the supervised machine learning could be used to predict suspected increased ICP in children. Although machine learning-based systems are powerful technologies as mentioned above, they should not replace the clinical judgment of physicians and medical teams [11][12][13][14]. The ideal role of these systems is as a data-driven input to the surgical decision-making process, designed to solve focused problems such as predicting the risk of increased ICP in this study.…”

“…Machine learning classification is one of the domains of AI that enables an algorithm or classifier to learn patterns in large, complex datasets and produce useful predictive outputs. The number of published machine learning studies in neurosurgery is increasing exponentially [11][12][13][14]. Some of them have focused on the application of machine learning algorithms to support clinical decision making in neurosurgery.…”

Brief Review of Machine Learning in Neurosurgery

“…Some of the interesting studies were face detection to diagnose Acromegaly, predict rupture of anterior communicating aneurysm, outcome in neurosurgical patients, prediction of trauma in motorcycleriders from Taiwan, impact of race on the discharge and length of hospitalization after brain tumor surgery, radiosurgery outcome in cerebral arteriovenous malformation, outcome after anterior cervical discectomy and a study of learning in patients with multiple concussions. 1,2,4,11,[14][15][16][17]22 Some of the common applications of ML in neurosurgery is given in Table 1.…”