Machine learning and neurosurgical outcome prediction: a systematic review

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

doi:10.1016/j.wneu.2017.09.149

Cited by 486Machine Learning in HealthcareArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Machine learning and neurosurgical outcome prediction: a systematic review

Open access

Senders, Joeky T., Staples, Patrick C., Karhade, Aditya V., Zaki, Mark M., Gormley, William B., Broekman, Marike L.D., Smith, Timothy R., Arnaout, Omar·2018·World Neurosurgery

DOI 10.1016/j.wneu.2017.09.149

Open this paper in CoChat — free account Open PDF

Abstract

Objective Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction.Methods A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017.Results Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts.Conclusions In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.wneu.2017.09.149

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

ML tools for optimizing care outcomes have been used in critical care for optimizing sepsis management, management of chronic conditions, and optimizing surgical outcomes.
“ML tools for optimizing care outcomes have been used in various settings, including critical care for optimizing sepsis management (27), management of chronic conditions (28), and optimizing surgical outcomes (29).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [29]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · see citing works
Machine Learning in Medicine
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Applications of Machine Learning in Cancer Prediction and Prognosis
Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,113 · see citing works
Applications of machine learning in cancer prediction and prognosis.
Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879 · see citing works
Natural and Artificial Intelligence in Neurosurgery: A Systematic Review
Senders, J. T., Arnaout, O. M., Karhade, A. V., Dasenbrock, H. H., Gormley, W. B., Broekman, M. L. D., & Smith, T. R. (2017) · Neurosurgery · doi:10.1093/neuros/nyx384 · Cited by 264 · see citing works
Self-citation
Artificial neural networks for diagnosis and survival prediction in colon cancer
Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 216 · see citing works
Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
Kang, J., Schwartz, R. S., Flíckinger, J. C., & Beriwal, S. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.07.2286 · Cited by 215 · see citing works
Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data
Munsell, B. C., Chong-yaw Wee, A., Keller, S. S., Weber, B., Elger, C. E., da Silva, L. A. T., Nesland, T., Styner, M. A., Shen, D. G., & Bonilha, L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.008 · Cited by 164 · see citing works
Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma
Akbari, H., Macyszyn, L. J., Da, X., Bilello, M., Wolf, R. L., Martinez‐Lage, M., Biros, G., Alonso‐Basanta, M., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001202 · Cited by 164 · see citing works
Magnetic resonance imaging pattern learning in temporal lobe epilepsy: Classification and prognostics
Bernhardt, B. C., Hong, S., Bernasconi, A., & Bernasconi, N. (2014) · Annals of Neurology · doi:10.1002/ana.24341 · Cited by 153 · see citing works
Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations
Oermann, E. K., Rubinsteyn, A., Ding, D., Mascitelli, J. R., Starke, R. M., Bederson, J. B., Kano, H., Lunsford, L. D., Sheehan, J. P., Hammerbacher, J., et al. (2016) · Scientific Reports · doi:10.1038/srep21161 · Cited by 122 · see citing works
Clinical epidemiology in the era of big data: new opportunities, familiar challenges
Ehrenstein, V., Nielsen, H., Pedersen, A. B., Johnsen, S. P., & Pedersen, L. H. (2017) · Clinical Epidemiology · doi:10.2147/clep.s129779 · Cited by 122 · see citing works
Application of Artificial Intelligence to the Management of Urological Cancer
Abbod, M., Catto, J. W. F., Linkens, D. A., & Hamdy, F. C. (2007) · The Journal of Urology · doi:10.1016/j.juro.2007.05.122 · Cited by 113 · see citing works
A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations
Emblem, K. E., Pinho, M. C., Zöllner, F. G., Due‐Tønnessen, P., Hald, J. K., Schad, L. R., Meling, T. R., Rapalino, O., & Bjørnerud, A. (2014) · Radiology · doi:10.1148/radiol.14140770 · Cited by 112 · see citing works
Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
Memarian, N., Kim, S., Dewar, S. R., Engel, J., & Staba, R. J. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.06.008 · Cited by 109 · see citing works
Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity
Akbari, H., Macyszyn, L. J., Da, X., Wolf, R. L., Bilello, M., Verma, R., O’Rourke, D. M., & Davatzikos, C. A. (2014) · Radiology · doi:10.1148/radiol.14132458 · Cited by 100 · see citing works
In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models
Shi, H., Hwang, S., Lee, K., & Lin, C. (2013) · Journal of neurosurgery · doi:10.3171/2013.1.jns121130 · Cited by 97 · see citing works
Functional Connectivity Estimated from Intracranial EEG Predicts Surgical Outcome in Intractable Temporal Lobe Epilepsy
Antony, A. R., Alexopoulos, A. V., Gonzalez-Martinez, J. A., Mosher, J. C., Jehi, L. E., Burgess, R. C., So, N. K., & Galán, R. F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077916 · Cited by 90 · see citing works
Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease
Shamir, R. R., Dolber, T., Noecker, A. M., Walter, B. L., & McIntyre, C. C. (2015) · Brain stimulation · doi:10.1016/j.brs.2015.06.003 · Cited by 81 · see citing works
Artificial neural networks in neurosurgery
Azimi, P., Mohammadi, H. R., Benzel, E. C., Shahzadi, S., Azhari, S., & Montazeri, A. (2014) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2014-307807 · Cited by 81 · see citing works
Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering
Emblem, K. E., Nedregaard, B., Hald, J. K., Nome, T., Due‐Tønnessen, P., & Bjørnerud, A. (2009) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21815 · Cited by 73 · see citing works
Outcomes and complications after endovascular treatment of Brain Arteriovenous Malformations: a Prognostication Attempt using artificial Intelligence
Asadi, H., Kok, H. K., Looby, S., Brennan, P., O'Hare, A., & Thornton, J. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.09.086 · Cited by 67 · see citing works
Prediction of Symptomatic Cerebral Vasospasm after Aneurysmal Subarachnoid Hemorrhage with an Artificial Neural Network: Feasibility and Comparison with Logistic Regression Models
Dumont, T. M., Rughani, A. I., & Tranmer, B. I. (2011) · World Neurosurgery · doi:10.1016/j.wneu.2010.07.007 · Cited by 66 · see citing works
Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
Armañanzas, R., Alonso‐Nanclares, L., Oroquieta, J. D. F., Kastanauskaite, A., de Sola, R. G., DeFelipe, J., Bielza, C., & Larrañaga, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062819 · Cited by 64 · see citing works
Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis
Azimi, P., Benzel, E. C., Shahzadi, S., Azhari, S., & Mohammadi, H. R. (2014) · Journal of Neurosurgery Spine · doi:10.3171/2013.12.spine13674 · Cited by 61 · see citing works
Chronic subdural hematoma outcome prediction using logistic regression and an artificial neural network
Abouzari, M., Rashidi, A., Zandi‐Toghani, M., Behzadi, M., & Asadollahi, M. (2009) · Neurosurgical Review · doi:10.1007/s10143-009-0215-3 · Cited by 57 · see citing works
Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis
Azimi, P., & Mohammadi, H. R. (2014) · Journal of Neurosurgery Pediatrics · doi:10.3171/2013.12.peds13423 · Cited by 55 · see citing works
Classification and Prediction of Clinical Improvement in Deep Brain Stimulation From Intraoperative Microelectrode Recordings
Kostoglou, K., Michmizos, K. P., Stathis, P. G., Sakas, D. E., Nikita, K. S., & Mitsis, G. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2591827 · Cited by 53 · see citing works
Predicting Outcome of Anterior Temporal Lobectomy Using Simulated Neural Networks
Grigsby, J., Kramer, R. E., Schneiders, J. L., Gates, J. R., & Smith, W. B. (1998) · Epilepsia · doi:10.1111/j.1528-1157.1998.tb01275.x · Cited by 49 · see citing works
Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy
Hoffman, H., Lee, S. I., Garst, J. H., Lu, D. S., Li, C. H., Nagasawa, D. T., Ghalehsari, N., Jahanforouz, N., Razaghy, M., Espinal, M., et al. (2015) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2015.04.002 · Cited by 45 · see citing works
Advanced [18F]FDG and [11C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis
Njiwa, J. Y., Gray, K. R., Costes, N., Mauguı̀ere, F., Ryvlin, P., & Hammers, A. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.11.013 · Cited by 44 · see citing works
Neural network analysis of preoperative variables and outcome in epilepsy surgery
Arle, J. E., Perrine, K. R., Devinsky, O., & Doyle, W. (1999) · Journal of neurosurgery · doi:10.3171/jns.1999.90.6.0998 · Cited by 43 · see citing works
Prediction of post-surgical seizure outcome in left mesial temporal lobe epilepsy
Feis, D., Schoene‐Bake, J., Elger, C. E., Wagner, J., Tittgemeyer, M., & Weber, B. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.06.010 · Cited by 42 · see citing works
Machine learning in preoperative glioma MRI: Survival associations by perfusion‐based support vector machine outperforms traditional MRI
Emblem, K. E., Due‐Tønnessen, P., Hald, J. K., Bjørnerud, A., Pinho, M. C., Scheie, D., Schad, L. R., Meling, T. R., & Zöllner, F. G. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24390 · Cited by 42 · see citing works
Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences
Lo, B., Macdonald, R. L., BAKER, A. J., & Levine, M. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/904860 · Cited by 41 · see citing works
Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network
Habibi, Z., Ertiaei, A., Nikdad, M., Mirmohseni, A. S., Afarideh, M., Heidari, V., Saberi, H., Rezai, A. S., & Nejat, F. (2016) · Child s Nervous System · doi:10.1007/s00381-016-3248-2 · Cited by 41 · see citing works
Use of Artificial Neural Networks to Predict Recurrent Lumbar Disk Herniation
Azimi, P., Mohammadi, H. R., Benzel, E. C., Shahzadi, S., & Azhari, S. (2014) · Journal of Spinal Disorders & Techniques · doi:10.1097/bsd.0000000000000200 · Cited by 39 · see citing works
Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients
Shamim, M. S., Enam, S. A., & Qidwai, U. A. (2009) · Surgical Neurology · doi:10.1016/j.surneu.2009.07.012 · Cited by 30 · see citing works
Survival of Patients With Multiple Intracranial Metastases Treated With Stereotactic Radiosurgery
Knoll, M. A., Oermann, E. K., Yang, A. I., Paydar, I., Steinberger, J. M., Collins, B. T., Collins, S., Ewend, M. G., & Kondziolka, D. S. (2016) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000000299 · Cited by 29 · see citing works
The prediction of successful surgery outcome in lumbar disc herniation based on artificial neural networks.
Azimi, P., Benzel, E. C., Shahzadi, S., Azhari, S., & Mohammadi, H. R. (2016) · PubMed · Cited by 24 · see citing works
Bullet Fragment of the Lumbar Spine: The Decision is More Important than the Incision
Moisi, M. D., Page, J., Gahramanov, S., & Oskouian, R. J. (2015) · Global Spine Journal · doi:10.1055/s-0035-1566231 · Cited by 17 · see citing works
Use of artificial neural networks to predict the probability of developing new cerebral metastases after radiosurgery alone
Azimi, P., Shahzadi, S., & Sadeghi, S. (2020) · Journal of Neurosurgical Sciences · doi:10.23736/s0390-5616.16.03479-2 · Cited by 13 · see citing works
Application of the Intelligent Techniques in Transplantation Databases: A Review of Articles Published in 2009 and 2010
Sousa, F. S., Hummel, A. D., Maciel, R. F., Cohrs, F. M., Falcão, A. E. J., Teixeira, F. O., Baptista, R., Mancini, F., Costa, T. M., Alves, D., et al. (2011) · Transplantation Proceedings · doi:10.1016/j.transproceed.2011.02.028 · Cited by 8 · see citing works

Flagged first, then by citations

Research works printed but not linked: 1

Breiman L. StatisticalModeling: the two cultures. Stat Sci. 2001;16:199-231.

Sorted by citations

Logistic regression was as good as machine learning for predicting major chronic diseases
Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., Wong, T. Y., & Cheng, C. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.002 · Cited by 569 · see citing works
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
“ML tools for optimizing care outcomes have been used in various settings, including critical care for optimizing sepsis management (27), management of chronic conditions (28), and optimizing surgical outcomes (29).”
Method
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review
Buchlak, Q. D., Esmaili, N., Leveque, J., Farrokhi, F. R., Bennett, C. C., Piccardi, M., & Sethi, R. K. (2019) · Neurosurgical Review · doi:10.1007/s10143-019-01163-8 · Cited by 226 · see citing works
“These statistics allow the comparison of the performance of different forms of supervised learning models and allow the researcher to determine the applicability of the model to clinical practice [36, 98].”
Background
The impact of machine learning on patient care: A systematic review
Ben-Israel, D., Jacobs, W. B., Casha, S., Lang, S., Ryu, W. H. A., de Lotbiniere-Bassett, M., & Cadotte, D. W. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101785 · Cited by 207 · see citing works
A systematic review of machine learning models for predicting outcomes of stroke with structured data
Wang, W., Kiik, M., Peek, N. B., Ćurčin, V., Marshall, I. J., Rudd, A. G., Wang, Y., Douiri, A., Wolfe, C. D. A., & Bray, B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234722 · Cited by 205 · see citing works
“We combined published PubMed search filters for stroke [8], ML [9], and prediction models [10].”
Method
An introduction and overview of machine learning in neurosurgical care
Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O. (2018) · Acta Neurochirurgica · doi:10.1007/s00701-017-3385-8 · Cited by 178 · see citing works
Self-citation
Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models
Ramkumar, P. N., Navarro, S. M., Haeberle, H. S., Karnuta, J. M., Mont, M. A., Iannotti, J. P., Patterson, B. M., & Krebs, V. E. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.12.030 · Cited by 143 · see citing works
Machine Learning in Vascular Surgery: A Systematic Review and Critical Appraisal
Li, B., Feridooni, T., Cuen-Ojeda, C., Kishibe, T., de Mestral, C., Mamdani, M., & Al-Omran, M. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00552-y · Cited by 142 · see citing works
“The authors demonstrated a median accuracy and AUROC of 94.5% and 0.83, respectively 51 .”
BackgroundMethodInfluential
Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation
Jung, D., & Choi, Y. (2021) · Minerals · doi:10.3390/min11020148 · Cited by 137 · see citing works
Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01284-z · Cited by 139 · see citing works
“In recent years, ML is increasingly used to predict a wide range of clinically relevant outcomes with the availability of health data, including cancer [25] or dementia [24] prognosis, neurosurgical outcomes [26, 89], and clinical diagnostic outcomes [31].”
Background
New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease
Gupta, R., Kumari, S., Senapati, A., Ambasta, R. K., & Kumar, P. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102013 · Cited by 134 · see citing works
Artificial Intelligence in Ovarian Cancer Diagnosis
Akazawa, M., & Hashimoto, K. (2020) · Anticancer Research · doi:10.21873/anticanres.14482 · Cited by 138 · see citing works
An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning
Senders, J. T., Staples, P. C., Mehrtash, A., Coté, D. J., TAPHOORN, M. J. B., Reardon, D. A., Gormley, W. B., Smith, T. R., Broekman, M. L. D., & Arnaout, O. M. (2019) · Neurosurgery · doi:10.1093/neuros/nyz403 · Cited by 128 · see citing works
Self-citation
Neurosurgery and artificial intelligence
Mofatteh, M. (2021) · AIMS neuroscience · doi:10.3934/neuroscience.2021025 · Cited by 128 · see citing works
“Furthermore, generating and training algorithms requires a vast amount of data [31].”
BackgroundInfluential
Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review
Campagnini, S., Arienti, C., Patrini, M., Liuzzi, P., Mannini, A., & Carrozza, M. C. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01032-4 · Cited by 128 · see citing works
“The recent growing interest is due to the increasing complexity and numerosity of available data sets, as well as the presence of multifactorial data with diverse origins, for which more classical methods do not allow accurate results [6, 7].”
Background
Current concepts and recent advances in understanding and managing lumbar spine stenosis
Bagley, C. A., MacAllister, M. C., Dosselman, L. J., Moreno, J., Aoun, S. G., & Ahmadieh, T. Y. E. (2019) · F1000Research · doi:10.12688/f1000research.16082.1 · Cited by 124 · see citing works
Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion
Karhade, A. V., Ogink, P. T., Thio, Q. C. B. S., Broekman, M. L. D., Cha, T. D., Hershman, S. H., Mao, J., Peul, W. C., Schoenfeld, A. J., Bono, C. M., et al. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.01.009 · Cited by 126 · see citing works
Self-citation
Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries
Feng, J., Wang, Y., Peng, J., Sun, M., Zeng, J., & Jiang, H. (2019) · Journal of Critical Care · doi:10.1016/j.jcrc.2019.08.010 · Cited by 113 · see citing works
Postoperative delirium prediction using machine learning models and preoperative electronic health record data
Bishara, A. M., Chiu, C., Whitlock, E. L., Douglas, V. C., Lee, S., Butte, A. J., Leung, J. M., & Donovan, A. L. (2022) · BMC Anesthesiology · doi:10.1186/s12871-021-01543-y · Cited by 112 · see citing works
“…offer better performance than traditional clinician-based regression models, both in this population and by comparison to published literature [40–42], implying that they could be used to more efficiently direct resources to patients at high risk compared with existing models [15] and with a…”
Result
Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G. S., Damen, J. A., Kirtley, S., Hooft, L., Riley, R. D., CALSTER, B. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.024 · Cited by 109 · see citing works
Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning
Paliwal, N., Jaiswal, P., Tutino, V. M., Shallwani, H., Davies, J. M., Siddiqui, A. H., Rai, R., & Meng, H. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18332 · Cited by 109 · see citing works
“20,30,31,35,36 The models include the standard statistical logistic regression (LR), support vector machine (SVM, with linear [L-SVM] and Gaussian [G-SVM] kernels), k-nearest neighbor (k-NN), and neural network (NN).”
Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models
Senanayake, S. J., White, N. M., Graves, N., Healy, H. G., Baboolal, K., & Kularatna, S. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103957 · Cited by 108 · see citing works
Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks
Hossain, M. A., Noor, R. M., Yau, K. A., Azzuhri, S. R., Z’aba, M. R., & Ahmedy, I. (2020) · IEEE Access · doi:10.1109/access.2020.2989870 · Cited by 107 · see citing works
Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: Gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models
Sumayli, A. (2023) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2023.104833 · Cited by 96 · see citing works
Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
Kompa, B., Hakim, J. B., Palepu, A. K., Almon, K. G., Smith, M. R., Bain, P. A., Woloszynek, S., Painter, J. L., Bate, A., & Beam, A. L. (2022) · Drug Safety · doi:10.1007/s40264-022-01176-1 · Cited by 93 · see citing works
Modeling brain dynamics after tumor resection using The Virtual Brain
Aerts, H., Schirner, M., Dhollander, T., Jeurissen, B., Achten, E., Roost, D. V., Ritter, P., & Marinazzo, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116738 · Cited by 88 · see citing works
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Shoeibi, A., Moridian, P., Khodatars, M., Ghassemi, N., Jafari, M., Alizadehsani, R., Kong, Y., Górriz, J. M., Ramı́rez, J., Khosravi, A., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106053 · Cited by 102 · see citing works
The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis
Tran, B. X., McIntyre, R. S., Latkin, C. A., Phan, H. T., Vu, G. T., Nguyen, H. L. T., Gwee, K. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16122150 · Cited by 93 · see citing works
“One of the few attempts to address this issue is a systematic review by Senders et al., which highlighted the great potential and e ﬀ ectiveness of machine learning in predicting neuropsychiatric outcomes [25].”
Background
Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review
Rahman, M. M., Paul, K. C., Hossain, M. A., Ali, G. G. M. N., Rahman, M. S., & Thill, J. (2021) · IEEE Access · doi:10.1109/access.2021.3079121 · Cited by 91 · see citing works
“and able to reveal the hidden patterns in the data set that were previously unknown [41]–[43].”
Background
Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future
Tangsrivimol, J. A., Schonfeld, E., Zhang, M., Veeravagu, A., Smith, T. R., Härtl, R., Lawton, M. T., Sherbini, A. E., Prevedello, D. M., Glicksberg, B. S., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13142429 · Cited by 86 · see citing works
Self-citation
Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients
DeVries, Z., Hoda, M., Rivers, C. S., Maher, A., Wai, E. K., Moravek, D., Stratton, A. C., Kingwell, S. P., Fallah, N., Paquet, J., et al. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.09.007 · Cited by 84 · see citing works
Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry
Goyal, A., Ngufor, C. G., Kerezoudis, P., McCutcheon, B. A., Storlie, C. B., & Bydon, M. (2019) · Journal of Neurosurgery Spine · doi:10.3171/2019.3.spine181367 · Cited by 80 · see citing works
Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery
Kazemzadeh, K., Akhlaghdoust, M., & Zali, A. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1241923 · Cited by 82 · see citing works
“appropriate patients for treatment, and guiding patients towards informed decisions (18).”
Background
Machine learning in neurosurgery: a global survey
Staartjes, V. E., Stumpo, V., Kernbach, J. M., Klukowska, A. M., Gadjradj, P. S., Schröder, M. L., Veeravagu, A., Stienen, M. N., van Niftrik, C. H. B., Serra, C., et al. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04532-1 · Cited by 78 · see citing works
Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning
Yao, L., Cai, M., Chen, Y., Shen, C., Shi, L., & Guo, Y. C. (2019) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2019.04.006 · Cited by 77 · see citing works
Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review
Ogink, P. T., Groot, O. Q., Karhade, A. V., Bongers, M. E. R., Öner, F. C., Verlaan, J., & Schwab, J. H. (2021) · Acta Orthopaedica · doi:10.1080/17453674.2021.1932928 · Cited by 77 · see citing works
Self-citation
Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm
Williams, S. C., Horsfall, H. L., Funnell, J. P., HANRAHAN, J. G., Khan, D. Z., Muirhead, W. R., Stoyanov, D. V., & Marcus, H. J. (2021) · Cancers · doi:10.3390/cancers13195010 · Cited by 77 · see citing works
Random Forest–Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy
Hanko, M., Grendár, M., Snopko, P., Opšenák, R., Šutovský, J., Benčo, M., Soršák, J., Zeleňák, K., & Kolarovszki, B. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.01.002 · Cited by 72 · see citing works
Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews
Martínez-Millana, A., Saez-Saez, A., Tornero-Costa, R., Muscat, N. A., Traver, V., & Novillo-Ortiz, D. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104855 · Cited by 73 · see citing works
Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture
Silva, M. A., Patel, J., Kavouridis, V. K., Gallerani, T., Beers, A. L., Chang, K., Hoebel, K., Brown, J. M., See, A. P., Gormley, W. B., et al. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.06.231 · Cited by 69 · see citing works
Self-citation
Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms
Farrokhi, F. R., Buchlak, Q. D., Sikora, M., Esmaili, N., Marsans, M., McLeod, P. S., Mark, J., Cox, E. J., Bennett, C. C., & Carlson, J. D. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.10.063 · Cited by 73 · see citing works
Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach
Abujaber, A. A., Fadlalla, A. M., Gammoh, D., Abdelrahman, H., Mollazehi, M., & El-Menyar, A. A. (2020) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-020-00738-5 · Cited by 68 · see citing works
Prediction Models in Aneurysmal Subarachnoid Hemorrhage: Forecasting Clinical Outcome With Artificial Intelligence
de Jong, G., Aquarius, R., Sanaan, B., Bartels, R., Grotenhuis, J. A., Henssen, D. J. H. A., & Boogaarts, H. D. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa581 · Cited by 66 · see citing works
Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020
Xia, F., Li, Q., Luo, X., & Wu, J. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.906774 · Cited by 66 · see citing works
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging
Hale, A. T., Stonko, D. P., Wang, L. A., Strother, M. K., & Chambless, L. B. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18191 · Cited by 67 · see citing works
Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models
Jin, H., Kim, Y.-G., Jin, Z., Rushchitc, A. A., & Al-Shati, A. S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.10.334 · Cited by 65 · see citing works
A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease
Wan, K. R., Maszczyk, T., See, A. A. Q., Dauwels, J., & King, N. K. K. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.09.018 · Cited by 63 · see citing works
Artificial intelligence. A tool for sports trauma prediction.
Kakavas, G., Malliaropoulos, N., Pruna, R., & Maffulli, N. (2020) · Injury · doi:10.1016/j.injury.2019.08.033 · Cited by 54 · see citing works
Big Data in Forecasting Research: A Literature Review
Tang, L., Li, J., Du, H., Li, L., Wu, J., & Wang, S. (2021) · Big Data Research · doi:10.1016/j.bdr.2021.100289 · Cited by 62 · see citing works
Machine learning applications for the prediction of surgical site infection in neurological operations
Tunthanathip, T., Sae-heng, S., Oearsakul, T., Sakarunchai, I., Kaewborisutsakul, A., & Taweesomboonyat, C. (2019) · Neurosurgical FOCUS · doi:10.3171/2019.5.focus19241 · Cited by 62 · see citing works
Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling
Liu, J., Xiong, Y., Zhong, M., Yang, Y., Guo, X., Tan, X., & Zhao, B. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa052 · Cited by 61 · see citing works
Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review
Cho, S. M., Austin, P. C., Ross, H. J., Abdel-Qadir, H. M., Chicco, D., TOMLINSON, G. A., Taheri, C., Foroutan, F., Lawler, P. R., Billia, F., et al. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.02.020 · Cited by 64 · see citing works
Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential
Tewarie, I. A., Senders, J. T., Kremer, S. W. F., Devi, S., Gormley, W. B., Arnaout, O. M., Smith, T. R., & Broekman, M. L. D. (2020) · Neurosurgical Review · doi:10.1007/s10143-020-01430-z · Cited by 57 · see citing works
Self-citation
Artificial Intelligence in Neurosurgery: A Bibliometric Analysis
El-Hajj, V. G., Gharios, M., Edström, E., & Terander, A. E. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.12.087 · Cited by 61 · see citing works
Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis
Nguyen, A. V. P., Blears, E. E., Ross, E., Lall, R. R., & Ortega-Barnett, J. R. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18325 · Cited by 58 · see citing works
Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach
Abujaber, A. A., Fadlalla, A. M., Gammoh, D., Abdelrahman, H., Mollazehi, M., & El-Menyar, A. A. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01363-z · Cited by 57 · see citing works
“, [17] systematically reviewed 30 publications that utilized machine learning approaches to predict mortality and other neurological outcomes following TBI.”
Background
Novel Machine Learning Approach for the Prediction of Hernia Recurrence, Surgical Complication, and 30-Day Readmission after Abdominal Wall Reconstruction
Hassan, A. M., Lu, S.-C., Asaad, M., Liu, J., Offodile, A. C., Sidey-Gibbons, C. J., & Butler, C. E. (2022) · Journal of the American College of Surgeons · doi:10.1097/xcs.0000000000000141 · Cited by 57 · see citing works
Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis
Courville, E., Kazim, S. F., Vellek, J., Tarawneh, O. H., Stack, J., Roster, K., Roy, J. M., Schmidt, M. H., & Bowers, C. A. (2023) · Surgical Neurology International · doi:10.25259/sni_312_2023 · Cited by 57 · see citing works
“In recent years, TBI research has employed several ML models for the prediction of patient events and outcomes; however, there exists much variability in their results.[40-42] Conflicting data continues to be reported in the literature; for example, while one study reported that the ML-based predictive models were mor…”
Result
Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores
Dengler, N. F., Madai, V. I., Unteroberdörster, M., Zihni, E., Brune, S. C., Hilbert, A., Livne, M., Wolf, S., Vajkoczy, P., & Frey, D. (2021) · Neurosurgical Review · doi:10.1007/s10143-020-01453-6 · Cited by 56 · see citing works
Topics and trends in artificial intelligence assisted human brain research
Chen, X., Chen, J., Cheng, G., & Gong, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231192 · Cited by 56 · see citing works
“(2018) [11] Machine learning for predicting neurosurgical outcome 30 Systematic review 1998– 2017 To offer an overview of the theoretical concepts of machine learning and to examine its usefulness to assist neurosurgical decision making, and to compare the performance of machine learning with prognostic indices, tradi…”
Background
Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods
Ogink, P. T., Karhade, A. V., Thio, Q. C. B. S., Gormley, W. B., Öner, F. C., Verlaan, J., & Schwab, J. H. (2019) · European Spine Journal · doi:10.1007/s00586-019-05928-z · Cited by 54 · see citing works
Self-citation
Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study
Bacchi, S., Gluck, S., Tan, Y., Chim, I., Cheng, J., Gilbert, T., Menon, D. K., Jannes, J., Kleinig, T. J., & Koblar, S. A. (2020) · Internal and Emergency Medicine · doi:10.1007/s11739-019-02265-3 · Cited by 54 · see citing works
Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges
Dagi, T. F., Barker, F. G., & Glass, J. L. (2021) · Neurosurgery · doi:10.1093/neuros/nyab170 · Cited by 53 · see citing works
Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty
Klemt, C., Harvey, M. J., Robinson, M. G., Esposito, J. G., Yeo, I., & Kwon, Y. (2022) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-021-06812-4 · Cited by 53 · see citing works
“Machine learning model assessment was performed through four different methods in concordance with the literature to provide a robust model assessment [34]: (1) discrimination (area under the receiver operating curve [AUC]), (2) calibration (calibration plot—intercept and slope), (3) Brier score, and (4) decision curv…”
Method
Machine learning applications in tobacco research: a scoping review
Fu, R., Kundu, A., Mitsakakis, N., Elton‐Marshall, T., Wang, W., Hill, S., Bondy, S. J., Hamilton, H. A., Selby, P. L., Schwartz, R. M., et al. (2021) · Tobacco Control · doi:10.1136/tobaccocontrol-2020-056438 · Cited by 51 · see citing works
“An apparent focus of these reviews is on supervised learning, including diagnosing and prognosing diseases using neuroimaging data, 11–14 predicting neurosurgical outcomes 15 and forecasting population health-related outcomes.”
Background
Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study
Choi, B. K., Kim, M. S., & Kim, S. H. (2020) · Journal of Tissue Viability · doi:10.1016/j.jtv.2020.06.002 · Cited by 50 · see citing works
Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis
Hoodbhoy, Z. A., Jiwani, U., Sattar, S., Salam, R. A., Hasan, B. S., & Das, J. K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.708365 · Cited by 50 · see citing works
“Useofarti ﬁ cial intelligence (AI) in healthcare and its utility in medicine, from diagnosis and risk assessment to outcome predictions for a wide variety of illnesses has been extensively described in the literature (Koivu et al., 2018; Senders et al., 2018; Harris et al., 2019).”
Background
Biosensors for Epilepsy Management: State-of-Art and Future Aspects
Tiwari, S., Sharma, V., Mujawar, M. A., Mishra, Y. K., Kaushik, A. K., & Ghosal, A. (2019) · Sensors · doi:10.3390/s19071525 · Cited by 51 · see citing works
The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning
Fonseca, Á. Y. G., Martín-Jiménez, C., Barreto, G. E., Aristizábal-Pachón, A. F., & González, J. (2021) · Biomolecules · doi:10.3390/biom11081132 · Cited by 48 · see citing works
Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery
Azad, T. D., Ehresman, J., Ahmed, A. K., Staartjes, V. E., Lubelski, D., Stienen, M. N., Veeravagu, A., & Ratliff, J. K. (2020) · The Spine Journal · doi:10.1016/j.spinee.2020.10.006 · Cited by 46 · see citing works
Machine learning-enabled multiplexed microfluidic sensors
Dabbagh, S. R., Rabbi, F., Doğan, Z., Yetisen, A. K., & Taşoğlu, S. (2020) · Biomicrofluidics · doi:10.1063/5.0025462 · Cited by 43 · see citing works
Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review
Zhang, T., Nikouline, A., Lightfoot, D., & Nolan, B. (2022) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2022.05.011 · Cited by 44 · see citing works
Using an artificial neural network to predict traumatic brain injury
Hale, A. T., Stonko, D. P., Lim, J., Guillamondegui, O. D., Shannon, C. N., & Patel, M. B. (2018) · Journal of Neurosurgery Pediatrics · doi:10.3171/2018.8.peds18370 · Cited by 44 · see citing works
Multimodal prognostic features of seizure freedom in epilepsy surgery
Alim‐Marvasti, A., Vakharia, V. N., & Duncan, J. S. (2022) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-327119 · Cited by 41 · see citing works
Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis
Ogink, P. T., Karhade, A. V., Thio, Q. C. B. S., Hershman, S. H., Cha, T. D., Bono, C. M., & Schwab, J. H. (2019) · European Spine Journal · doi:10.1007/s00586-019-05936-z · Cited by 41 · see citing works
Self-citation
A traumatic brain injury prognostic model to support in-hospital triage in a low-income country: a machine learning–based approach
Rocha, T. A. H., Elahi, C., da Silva, N. C., Sakita, F. M., Fuller, A. T., Mmbaga, B. T., Green, E. P., Haglund, M. M., Staton, C. A., & Vissoci, J. R. N. (2019) · Journal of neurosurgery · doi:10.3171/2019.2.jns182098 · Cited by 41 · see citing works
In Reply: Big Data Research in Neurosurgery: A Critical Look at this Popular New Study Design
Oravec, C. S., Motiwala, M., Reed, K., Kondziolka, D. S., Barker, F. G., Michael, L. M., & Klimo, P. (2018) · Neurosurgery · doi:10.1093/neuros/nyy102 · Cited by 41 · see citing works
Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap Necrosis
Hassan, A. M., Biaggi, A. P., Asaad, M., Andejani, D. F., Liu, J., Offodile2nd, A. C., Selber, J. C., & Butler, C. E. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005386 · Cited by 38 · see citing works
Prediction of mortality following pediatric heart transplant using machine learning algorithms
Miller, R., Tumin, D., Cooper, J. N., Hayes, D., & Tobias, J. D. (2019) · Pediatric Transplantation · doi:10.1111/petr.13360 · Cited by 40 · see citing works
Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery
Voglis, S., van Niftrik, C. H. B., Staartjes, V. E., Brandi, G., Tschopp, O., Regli, L., & Serra, C. (2020) · Pituitary · doi:10.1007/s11102-020-01056-w · Cited by 40 · see citing works
The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review
Olawade, D. B., Marinze, S., Qureshi, N., Weerasinghe, K., & Teke, J. (2025) · Current Research in Translational Medicine · doi:10.1016/j.retram.2025.103493 · Cited by 39 · see citing works
Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?
Zoli, M., Staartjes, V. E., Guaraldi, F., Friso, F., Rustici, A., Asioli, S., Sollini, G., Pasquini, E., Regli, L., Serra, C., et al. (2020) · Neurosurgical FOCUS · doi:10.3171/2020.3.focus2060 · Cited by 39 · see citing works
The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction
Asaad, M., Lu, S.-C., Hassan, A. M., Kambhampati, P. V., Mitchell, D., Chang, E. I.-F., Yu, P., Hanasono, M. M., & Sidey-Gibbons, C. J. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-022-13053-3 · Cited by 40 · see citing works
Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis
Mehta, D., Gonzalez, X. T., Huang, G. T.-W., & Abraham, J. (2024) · British Journal of Anaesthesia · doi:10.1016/j.bja.2024.08.007 · Cited by 38 · see citing works
“13 A similar but broader systematic review of ML-driven models in surgical settings found that these algorithms used for postoperative predictive outcome models and risk stratiﬁca-tion were more accurate than validated prognostic scores and traditional statistics.”
Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network
Marcus, A. P., Marcus, H. J., Camp, S., Nandi, D., Kitchen, N. D., & Thorne, L. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-62160-2 · Cited by 36 · see citing works
Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
Lee, J. (2020) · Journal of Medical Internet Research · doi:10.2196/19918 · Cited by 36 · see citing works
“This gap in the literature is coherent across a wide range of medical specialties and diseases, including trauma [9], cancer [11], neurosurgery [10], depression [12], acute gastrointestinal bleeding [13], sepsis [14], acute liver failure [15], ischemic stroke [16], thermal injury [17], and…”
Background
Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning
Harrison, C. J., Loe, B. S., Lis, P., & Sidey-Gibbons, C. J. (2020) · Journal of Medical Internet Research · doi:10.2196/20950 · Cited by 35 · see citing works
“A great deal of enthusiasm has been shown for machine learning, as it has demonstrated exceptional performance in a variety of tasks including predicting the outcome of individuals following certain medical interventions, interpreting diagnostic images, and assessing the meaning of open-text passages [17-20].”
Background
Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms
Wu, X., Sun, Y., Xu, X. L., Steyerberg, E. W., Helmrich, I. R., Lecky, F. E., Guo, J., Li, X., Feng, J., Mao, Q., et al. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2022.0221 · Cited by 34 · see citing works
Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation following Implant-Based Reconstruction
Hassan, A. M., Biaggi-Ondina, A. P., Asaad, M., Morris, N., Liu, J., Selber, J. C., & Butler, C. E. (2023) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000010345 · Cited by 37 · see citing works
Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy
Sinclair, B., Cahill, V., Seah, J. C. Y., Kitchen, A., Vivash, L. E., Chen, Z. Z., Malpas, C. B., O’Shea, M. F., Desmond, P. M., Hicks, R. J., et al. (2022) · Epilepsia · doi:10.1111/epi.17217 · Cited by 33 · see citing works
Prediction of preterm birth using artificial intelligence: a systematic review
Akazawa, M., & Hashimoto, K. (2022) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2022.2056828 · Cited by 38 · see citing works
“Accuracy refers to the proportion of correct predictions among the total number of predictions, and AUC corresponds to the probability that a binary classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance (Senders et al. 2018).”
Background
Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review
Tricco, A. C., Hezam, A., Parker, A., Nincic, V., Harris, C., Fennelly, O., Thomas, S. M., Ghassemi, M., McGowan, J., Paprica, P. A., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-065845 · Cited by 32 · see citing works
“To code the implementation strategies, the modified Effective Practice and Organisation of Care (EPOC) classification was used 22 ; descriptions of categories can be found in online supplemental appendix 6.”
A Machine Learning Approach to Predict Postoperative Pancreatic Fistula After Pancreaticoduodenectomy Using Only Preoperatively Known Data
Ashraf‐Ganjouei, A., Romero‐Hernández, F., Wang, J., Casey, M., Frye, W., Hoffman, D. B., Hirose, K., Nakakura, E. K., Corvera, C. U., Maker, A. V., et al. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-023-14041-x · Cited by 32 · see citing works
Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion
Rudisill, S. S., Hornung, A. L., Barajas, J. N., Bridge, J. J., Mallow, G. M., Lopez, W. Y., Sayari, A. J., Louie, P. K., Harada, G. K., Tao, Y., et al. (2022) · European Spine Journal · doi:10.1007/s00586-022-07238-3 · Cited by 32 · see citing works
Ovary Cancer Diagnosing Empowered with Machine Learning
Taleb, N., Mehmood, S., Zubair, M., Naseer, I., Mago, B., & Nasir, M. U. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759010 · Cited by 35 · see citing works
“For starters, AI is not the same as traditional computer programming [10].”
Background
Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy
Leiter, R. E., Santus, E., Jin, Z., Lee, K. C., Yusufov, M., Chien, I., Ramaswamy, A., Moseley, E. T., Qian, Y., Schrag, D., et al. (2020) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2020.06.010 · Cited by 31 · see citing works
Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study
Staartjes, V. E., Sebök, M., Blum, P. G., Serra, C., Germans, M. R., Krayenbühl, N., Regli, L., & Esposito, G. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04355-0 · Cited by 30 · see citing works
Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review
Noor, N. S. E. M., & Ibrahim, H. (2020) · IEEE Access · doi:10.1109/access.2020.2998934 · Cited by 30 · see citing works
“ML, which is a ﬁeld of computer science, falls into a subdivision of artiﬁcial intelligence (AI) that enables algorithms to learn patterns in large, complex datasets without being explicitly programmed [19], [20].”
BackgroundMethodInfluential
Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery
Mantelakis, A., Assael, Y., Sorooshian, P., & Khajuria, A. (2021) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000003638 · Cited by 30 · see citing works
Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage
Trevisi, G., Caccavella, V. M., Scerrati, A., Signorelli, F., Salamone, G. G., Orsini, K., Fasciani, C., D’Arrigo, S., Auricchio, A., D’Onofrio, G. F., et al. (2022) · Neurosurgical Review · doi:10.1007/s10143-022-01802-7 · Cited by 29 · see citing works
“Recently, machine learning (ML) has emerged as a powerful tool to develop predictive algorithms from a large amount of data [35].”
Background

An introduction and overview of machine learning in neurosurgical care
Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O. (2018) · Acta Neurochirurgica · doi:10.1007/s00701-017-3385-8 · Cited by 178 · see citing works
Self-citation
Machine Learning and Surgical Outcomes Prediction: A Systematic Review
Elfanagely, O., Toyoda, Y., Othman, S., Mellia, J. A., Basta, M. N., Liu, T., Kording, K. P., Ungar, L., & Fischer, J. P. (2021) · Journal of Surgical Research · doi:10.1016/j.jss.2021.02.045 · Cited by 126 · see citing works
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review
Buchlak, Q. D., Esmaili, N., Leveque, J., Farrokhi, F. R., Bennett, C. C., Piccardi, M., & Sethi, R. K. (2019) · Neurosurgical Review · doi:10.1007/s10143-019-01163-8 · Cited by 226 · see citing works
Outcome Prediction in Brain Surgery
Gendreau, J. L., Amatya, B., Pulya, R., Gowda, K., Horowitz, M., & Mukherjee, D. (2026) · CRC Press eBooks · doi:10.1201/9781003471165-4
Natural and Artificial Intelligence in Neurosurgery: A Systematic Review
Senders, J. T., Arnaout, O. M., Karhade, A. V., Dasenbrock, H. H., Gormley, W. B., Broekman, M. L. D., & Smith, T. R. (2017) · Neurosurgery · doi:10.1093/neuros/nyx384 · Cited by 264 · see citing works
Self-citation

Authors: Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O.
Affiliations: Brigham and Women's Hospital (US)
Harvard University (US)
Corresponding author: Arnaout, Omar
ORCID: Senders, Joeky T.
Staples, Patrick C.
Karhade, Aditya V.
Zaki, Mark M.
Gormley, William B.
Broekman, Marike L.D.
Smith, Timothy R.
Arnaout, Omar
Venue: World Neurosurgery
Publisher: Elsevier BV
Published: 2018-01
Volume: 109
Pages: 476-486.e1
ISSN: 1878-8750 (ISSN-L)
DOI: 10.1016/j.wneu.2017.09.149
OpenAlex: W2761529114
Cited by: 486
References: 47
Citations by year: 2026: 49 · 2025: 51 · 2024: 63 · 2023: 68 · 2022: 85 · 2021: 67 · 2020: 58 · 2019: 31 · 2018: 13 · 2017: 1
FWCI: 4.10
Type: journal article
Language: en
Open access: green
scholarlypublications.universiteitleiden.nl
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: World Neurosurgery (publishedVersion)
PubMed (publishedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: machine learning, surgical decision-making, epilepsy surgery, brain tumor surgery, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, hydrocephalus, survival prediction, recurrence prediction, adverse events, prognostic score, clinical decision support
MeSH terms: Machine Learning, Computer Simulation, Decision Support Techniques, Humans, Nervous System Diseases, Prognosis, Outcome Assessment, Health Care, Neurosurgical Procedures

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Machine learning and neurosurgical outcome prediction: a systematic review

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks