An introduction and overview of machine learning in neurosurgical care

Senders, Joeky T.; Zaki, Mark M.; Karhade, Aditya V.; Chang, Brian M.; Gormley, William B.; Broekman, Marike L. D.; Smith, Timothy R.; Arnaout, Omar

doi:10.1007/s00701-017-3385-8

Cited by 132 publications

(111 citation statements)

References 61 publications

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“…and epilepsy. 3,14,17,19,23,24,26 Memarian et al 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. 15,17 Moreover, Armañanzas et al 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.…”

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confidence: 99%

Machine learning applications for the prediction of surgical site infection in neurological operations

Tunthanathip¹,

Saeheng²,

Oearsakul³

et al. 2019

Neurosurgical Focus

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OBJECTIVESurgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations.METHODSA retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed.RESULTSData were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%.CONCLUSIONSThe naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.

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confidence: 99%

Machine learning applications for the prediction of surgical site infection in neurological operations

Tunthanathip¹,

Saeheng²,

Oearsakul³

et al. 2019

Neurosurgical Focus

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“…To the best of our knowledge, this is the first study describing the use of an ANN to predict surgical resectability in patients with GBM. However, machine learning has been used elsewhere for neurosurgical outcome prediction in patients with brain tumours and other conditions such as neurovascular disease, epilepsy, movement disorders, traumatic brain injury, and hydrocephalus 8,19,20 . A recent systematic review has found that machine learning models perform significantly better than logistic regression, with a median absolute improvement in the AUC and accuracy of 0.06 and 15% respectively 8 .…”

Section: Discussionmentioning

confidence: 99%

Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network

Marcus

Camp

et al. 2020

Sci Rep

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In managing a patient with glioblastoma (GBM), a surgeon must carefully consider whether sufficient tumour can be removed so that the patient can enjoy the benefits of decompression and cytoreduction, without impacting on the patient's neurological status. In a previous study we identified the five most important anatomical features on a pre-operative MRi that are predictive of surgical resectability and used them to develop a simple, objective, and reproducible grading system. The objective of this study was to apply an artificial neural network (ANN) to improve the prediction of surgical resectability in patients with GBM. prospectively maintained databases were searched to identify adult patients with supratentorial GBM that underwent craniotomy and resection. performance of the Ann was evaluated against logistic regression and the standard grading system by analysing their Receiver operator characteristic (Roc) curves; Area Under curve (AUc) and accuracy were calculated and compared using Wilcoxon signed rank test with a value of p < 0.05 considered statistically significant. In all, 135 patients were included, of which 33 (24.4%) were found to have complete excision of all contrast-enhancing tumour. The AUC and accuracy were significantly greater using the ANN compared to the standard grading system (0.87 vs. 0.79 and 83% vs. 80% respectively; p < 0.01 in both cases). In conclusion, an Ann allows for the improved prediction of surgical resectability in patients with GBM.

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“…More recently, ANNs have been shown to robustly predict complications, outcomes, and prognosis among numerous fields, 5,13,31,[34][35][36] including TBI. 8,18,26,32,37,38 Thus, an ANN tool yielding predictive information concerning CRTBI would be helpful and provide an evidence-based mechanism for treating these patients. ANNs are computational constructs used to interpret the maximum number of combinations of data in complex systems, such as making medical diagnoses in neurosurgery, 36,37 where many competing factors influence outcome.…”

Section: Discussionmentioning

confidence: 99%

Using an artificial neural network to predict traumatic brain injury

Hale

Stonko

Lim

et al. 2019

Journal of Neurosurgery: Pediatrics

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OBJECTIVEPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling in patients who will have a clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based way to safely discharge children who are at low risk for a CRTBI. The authors hypothesized that an artificial neural network (ANN) trained on clinical and radiologist-interpreted imaging metrics could provide a tool for identifying patients likely to suffer from a CRTBI.METHODSThe authors used the prospectively collected, publicly available, multicenter Pediatric Emergency Care Applied Research Network (PECARN) TBI data set. All patients under the age of 18 years with TBI and admission head CT imaging data were included. The authors constructed an ANN using clinical and radiologist-interpreted imaging metrics in order to predict a CRTBI, as previously defined by PECARN: 1) neurosurgical procedure, 2) intubation > 24 hours as direct result of the head trauma, 3) hospitalization ≥ 48 hours and evidence of TBI on a CT scan, or 4) death due to TBI.RESULTSAmong 12,902 patients included in this study, 480 were diagnosed with CRTBI. The authors’ ANN had a sensitivity of 99.73% with precision of 98.19%, accuracy of 97.98%, negative predictive value of 91.23%, false-negative rate of 0.0027%, and specificity for CRTBI of 60.47%. The area under the receiver operating characteristic curve was 0.9907.CONCLUSIONSThe authors are the first to utilize artificial intelligence to predict a CRTBI in a clinically meaningful manner, using radiologist-interpreted CT information, in order to identify pediatric patients likely to suffer from a CRTBI. This proof-of-concept study lays the groundwork for future studies incorporating iterations of this algorithm directly into the electronic medical record for real-time, data-driven predictive assistance to physicians.

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An introduction and overview of machine learning in neurosurgical care

Cited by 132 publications

References 61 publications

Machine learning applications for the prediction of surgical site infection in neurological operations

Machine learning applications for the prediction of surgical site infection in neurological operations

Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network

Using an artificial neural network to predict traumatic brain injury

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