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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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Westwood M, Ramaekers B, Grimm S, Armstrong N, Wijnen B, Ahmadu C, de Kock S, Noake C, Joore M. Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis. Health Technol Assess 2024; 28:1-204. [PMID: 38512017 PMCID: PMC11017149 DOI: 10.3310/rdpa1487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
Background Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke. Objectives To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting. Methods Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care. Results A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher. Limitations and conclusions The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective. Future work Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice. Study registration This study is registered as PROSPERO CRD42021269609. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
| | | | | | - Ben Wijnen
- Kleijnen Systematic Reviews (KSR) Ltd, York, UK
| | | | | | - Caro Noake
- Kleijnen Systematic Reviews (KSR) Ltd, York, UK
| | - Manuela Joore
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
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Hokkinen L, Mäkelä T, Savolainen S, Kangasniemi M. Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation. BJR Open 2024; 6:tzae001. [PMID: 38352187 PMCID: PMC10860582 DOI: 10.1093/bjro/tzae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 12/03/2023] [Accepted: 12/26/2023] [Indexed: 02/16/2024] Open
Abstract
Objectives CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods. Methods The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed. Results The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (r = 0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (r = 0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121). Conclusions This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation. Advances in knowledge CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.
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Affiliation(s)
- Lasse Hokkinen
- Radiology, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki 00290, Finland
| | - Teemu Mäkelä
- Radiology, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki 00290, Finland
- Department of Physics, University of Helsinki, Helsinki 00014, Finland
| | - Sauli Savolainen
- Radiology, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki 00290, Finland
- Department of Physics, University of Helsinki, Helsinki 00014, Finland
| | - Marko Kangasniemi
- Radiology, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki 00290, Finland
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Shahrouki P, Kihira S, Tavakkol E, Qiao JX, Vagal A, Khatri P, Bahr-Hosseini M, Colby GP, Jahan R, Duckwiler G, Szeder V, Ledbetter L, Cai S, Salehi B, Doshi AH, Belani P, Fifi JT, De Leacy R, Mocco J, Saver JL, Liebeskind DS, Nael K. Automated assessment of ischemic core on non-contrast computed tomography: a multicenter comparative analysis with CT perfusion. J Neurointerv Surg 2023:jnis-2023-020954. [PMID: 37918907 DOI: 10.1136/jnis-2023-020954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 10/13/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using non-contrast CT (NCCT). OBJECTIVE To assess the performance of the e-Stroke Suite software (Brainomix) in assessing ischemic core volumes on NCCT compared with CT perfusion (CTP) in patients with acute ischemic stroke. METHODS In this retrospective multicenter study, patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP, successful reperfusion (modified Thrombolysis in Cerbral Infarction ≥2b), and post-treatment MRI, were included from three stroke centers. Automated calculation of ischemic core volumes was obtained on NCCT scans using ML algorithm deployed by e-Stroke Suite and from CTP using Olea software (Olea Medical). Comparative analysis was performed between estimated core volumes on NCCT and CTP and against MRI calculated final infarct volume (FIV). RESULTS A total of 111 patients were included. Estimated ischemic core volumes (mean±SD, mL) were 20.4±19.0 on NCCT and 19.9±18.6 on CTP, not significantly different (P=0.82). There was moderate (r=0.40) and significant (P<0.001) correlation between estimated core on NCCT and CTP. The mean difference between FIV and estimated core volume on NCCT and CTP was 29.9±34.6 mL and 29.6±35.0 mL, respectively (P=0.94). Correlations between FIV and estimated core volume were similar for NCCT (r=0.30, P=0.001) and CTP (r=0.36, P<0.001). CONCLUSIONS Results show that ML-based estimated ischemic core volumes on NCCT are comparable to those obtained from concurrent CTP in magnitude and in degree of correlation with MR-assessed FIV.
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Affiliation(s)
- Puja Shahrouki
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Elham Tavakkol
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Joe X Qiao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Achala Vagal
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Pooja Khatri
- Department of Neurology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Mersedeh Bahr-Hosseini
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Geoffrey P Colby
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Reza Jahan
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Gary Duckwiler
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Viktor Szeder
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Luke Ledbetter
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Stephen Cai
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Banafsheh Salehi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Amish H Doshi
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Puneet Belani
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Johanna T Fifi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Reade De Leacy
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - J Mocco
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jeffrey L Saver
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - David S Liebeskind
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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Shen GC, Hang Y, Ma G, Lu SS, Wang C, Shi HB, Wu FY, Xu XQ, Liu S. Prognostic value of multiphase CT angiography: estimated infarct core volume in the patients with acute ischaemic stroke after mechanical thrombectomy. Clin Radiol 2023; 78:e815-e822. [PMID: 37607843 DOI: 10.1016/j.crad.2023.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 07/15/2023] [Accepted: 07/26/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND AND PURPOSE Recent studies reported the feasibility of quantifying a reliable infarct core (IC) volume using multiphase computed tomography (mCTA) based on deep learning, however its prognostic value was not fully clarified. Therefore, we aimed to evaluate the prognostic value of mCTA-estimated IC volume in patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT). MATERIALS AND METHODS We retrospectively reviewed patients who underwent mCTA and MT for large vessel occlusion in middle cerebral artery and (or) internal carotid artery within 6 hours after symptom onset between January 2018 and November 2019. Patients were dichotomized into good (modified Rankin Scale [mRS] score, 0-2) and poor (mRS, 3-6) outcome groups. mCTA-estimated IC volume were generated based on a multi-scale three-dimensional convolutional neural network. Univariate, multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were used to identify the independent variables, and evaluate their performances in predicting the clinical outcome. RESULTS Of 44 included patients, 27 (61.4%) patients achieved good outcome. National Institutes of Health Stroke Scale scores at admission [NIHSSpre] (odds ratio [OR], 1.191; 95%confidence interval [CI], 1.028-1.379; P=0.020) and mCTA-estimated IC volume (OR, 1.076; 95%CI, 1.016-1.140; P=0.013) were found to be independently associated with functional outcome in patients with AIS after MT. After integrating NIHSSpre and mCTA-estimated IC volume, optimal performance (area under the ROC curve, 0.874; 95%CI, 0.739-0.954) could be obtained in predicting the clinical outcome. CONCLUSIONS mCTA-estimated IC volume might be promising for predicting the prognosis, and assisting in making individualized treatment decision in patients with AIS.
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Affiliation(s)
- G-C Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Y Hang
- Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - G Ma
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - C Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - H-B Shi
- Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - F-Y Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - X-Q Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - S Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Ruksakulpiwat S, Phianhasin L, Benjasirisan C, Schiltz NK. Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review. J Multidiscip Healthc 2023; 16:2593-2602. [PMID: 37674890 PMCID: PMC10478777 DOI: 10.2147/jmdh.s421280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/15/2023] [Indexed: 09/08/2023] Open
Abstract
Objective To evaluate the evidence of artificial neural network (NNs) techniques in diagnosing ischemic stroke (IS) in adults. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilized as a guideline for this review. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched to identify studies published between 2018 and 2022, reporting using NNs in IS diagnosis. The Critical Appraisal Checklist for Diagnostic Test Accuracy Studies was adopted to evaluate the included studies. Results Nine studies were included in this systematic review. Non-contrast computed tomography (NCCT) (n = 4 studies, 26.67%) and computed tomography angiography (CTA) (n = 4 studies, 26.67%) are among the most common features. Five algorithms were used in the included studies. Deep Convolutional Neural Networks (DCNNs) were commonly used for IS diagnosis (n = 3 studies, 33.33%). Other algorithms including three-dimensional convolutional neural networks (3D-CNNs) (n = 2 studies, 22.22%), two-stage deep convolutional neural networks (Two-stage DCNNs) (n = 2 studies, 22.22%), the local higher-order singular value decomposition denoising algorithm (GL-HOSVD) (n = 1 study, 11.11%), and a new deconvolution network model based on deep learning (AD-CNNnet) (n = 1 study, 11.11%) were also utilized for the diagnosis of IS. Conclusion The number of studies ensuring the effectiveness of NNs algorithms in IS diagnosis has increased. Still, more feasibility and cost-effectiveness evaluations are needed to support the implementation of NNs in IS diagnosis in clinical settings.
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Affiliation(s)
- Suebsarn Ruksakulpiwat
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Lalipat Phianhasin
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | | | - Nicholas K Schiltz
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
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Chung KJ, Khaw AV, Pandey SK, Lee DH, Mandzia JL, Lee TY. Feasibility of deconvolution-based multiphase CT angiography perfusion maps in acute ischemic stroke: Simulation and concordance with CT perfusion. J Stroke Cerebrovasc Dis 2022; 31:106844. [DOI: 10.1016/j.jstrokecerebrovasdis.2022.106844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/20/2022] [Accepted: 10/06/2022] [Indexed: 11/07/2022] Open
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Zhou Z, Huang C, Fu P, Huang H, Zhang Q, Wu X, Yu Q, Sun Y. Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury. CNS Neurosci Ther 2022; 29:181-191. [PMID: 36258296 PMCID: PMC9804086 DOI: 10.1111/cns.13993] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/18/2022] [Accepted: 09/23/2022] [Indexed: 02/06/2023] Open
Abstract
AIMS Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis. METHODS This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best-performed prediction model for in-hospital hypokalemia. The internal fivefold cross-validation and external validation were performed to demonstrate the interpretability and generalizability. RESULTS A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate-to-severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open-assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions. CONCLUSIONS The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.
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Affiliation(s)
- Zhengyu Zhou
- Department of Anesthesia, Huashan HospitalFudan UniversityShanghaiChina
| | - Chiungwei Huang
- Health Consultation and Physical Examination Center, Zhongshan HospitalFudan UniversityShanghaiChina,Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Pengfei Fu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Hong Huang
- Information Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Qi Zhang
- Information Center, Huashan HospitalFudan UniversityShanghaiChina
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina,National Center for Neurological DisordersShanghaiChina,Shanghai Key Laboratory of Brain Function Restoration and Neural RegenerationShanghaiChina,Neurosurgical Institute of Fudan UniversityShanghaiChina,Shanghai Clinical Medical Center of NeurosurgeryShanghaiChina
| | - Qiong Yu
- Department of Anesthesia, Huashan HospitalFudan UniversityShanghaiChina
| | - Yirui Sun
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina,National Center for Neurological DisordersShanghaiChina,Shanghai Key Laboratory of Brain Function Restoration and Neural RegenerationShanghaiChina,Neurosurgical Institute of Fudan UniversityShanghaiChina,Shanghai Clinical Medical Center of NeurosurgeryShanghaiChina
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