Neural network–based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery

Staartjes, Victor E.; Zattra, Costanza M; Akeret, Kevin; Maldaner, Nicolai; Muscas, Giovanni; Niftrik, Christiaan Hendrik Bas van; Fierstra, Jorn; Regli, Luca; Serra, Carlo

doi:10.3171/2019.4.jns19477

Cited by 33 publications

(55 citation statements)

References 26 publications

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“…The same group also was able to show an improvement in predictions of arteriovenous malformation radiosurgery outcomes [23]. Staartjes et al found that a deep learning approach was significantly better at predicting intraoperative cerebrospinal fluid leaks and gross total resection in pituitary surgery than logistic regression, while no predictors could be identified using traditional interferential statistics for the former outcome [34,37].…”

Section: Discussionmentioning

confidence: 98%

“…With the exponential growth of data in the era of big data, it is increasingly important to provide clinicians with tools for integrating this individual patient data into reliable prediction models. The latter primarily aims to enhance the surgical decision-making processes and potentially improve outcomes, but predictive analytics furthermore harbour the potential to reduce unnecessary health-care costs [21,29,31,34,36,37,41].…”

Section: Introductionmentioning

confidence: 99%

“…At present, the neurosurgical literature is increasingly focusing on substituting traditional statistical models with more complex ML models with the aim of improving predictive power [29,31]. For example, ML has been used in neurosurgery to predict post-operative satisfaction [2], early postoperative complications [41] or cerebrospinal fluid leaks [37]. Despite this encouraging trend and the presence of recent publications reviewing the large range of publications on ML in neurosurgery [28][29][30], data on the worldwide adoption and perception of ML in our specialty are currently lacking.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in neurosurgery: a global survey

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Background Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning in neurosurgery: a global survey

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“…The potentials of machine learning techniques in medicine and neurosurgery have been widely tested, and their employment in diagnostic and prognostic tasks is becoming more and more common given their abilities to outperform human capacity and traditional statistics [18,38,41,42,44]. Machine learning can be considered an evolution of traditional statistics, and there is no clear line dividing them [3].…”

Section: Discussionmentioning

confidence: 99%

“…Modern standards of data analysis and prediction models rely on machine learning (ML), a branch of statistical analysis that is gaining more and more consideration in the medical field due to its excellent results and, more recently, also in neurosurgery [6,10,18,34,41,42,44]. ML consists of algorithm-based models with the ability to learn and perform tasks that are not explicitly programmed, to improve the performances with experience (i.e., when the model analyzes new data), and to work with a large amount of data and nonlinear associations, where classical statistical methods can show some limitations [6,18,38].…”

Section: Introductionmentioning

confidence: 99%

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

et al. 2020

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Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.

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