Cited by 178Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.

An introduction and overview of machine learning in neurosurgical care

Open access

Senders, Joeky T., Zaki, Mark M., Karhade, Aditya V., Chang, Bliss, Gormley, William B., Broekman, Marike L., Smith, Timothy R., Arnaout, Omar·2018·Acta Neurochirurgica

DOI 10.1007/s00701-017-3385-8

Open this paper in CoChat — free account Open PDF

Abstract

Background Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care.Method A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.Results Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.Conclusions ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

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 · 10.1007/s00701-017-3385-8

Cites

66 of 66 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?

Self-check

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

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

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
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
Machine Learning in Medicine
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
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
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995
Toward a Shared Vision for Cancer Genomic Data
Grossman, R. L., Heath, A. P., Ferretti, V., Varmus, H. E., Lowy, D. R., Kibbe, W. A., & Staudt, L. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1607591 · Cited by 1,680
Machine learning and neurosurgical outcome prediction: a systematic review
Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2017.09.149 · Cited by 486
Self-citation
Machine Learning and Decision Support in Critical Care
Johnson, A. E. W., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2501978 · Cited by 459

Show all 68 references

Research works printed but not linked: 5

Hamzei-Sichani F, Sperling M, Fuertinger S, Sharan A, Simonyan K (2016) Cortical networks high frequency EEG activity patterns in patients undergoing epilepsy surgery. J Neurosurg 124:A1184
Mitchell TM (1997) Machine learning. McGraw-Hill Science, New York
Orringer D, Ji M, Lewis S, Camelo-Piragua S, Johnson T, Sagher O, Wang A, Maher C, Heth J, Xie X (2015) Visualizing brain tumor infiltration with stimulated Raman scattering microscopy. J Neurosurg 123:A523
Shamir R, Duchin Y, Kim JJK, Marmor O, Bergman H, Vitek JL, Sapiro G, Bick AS, Eliyahu R, Eitan R et al (2016) MER validation of a new targeting approach for STN-DBS surgery based on machine-learning and 7T-MRI database (10661). Neuromodulation 19:e67
Shih JJ, Krusienski DJ (2009) Electrocorticography in a brain-computer interface (BCI) paradigm. Epilepsia 50:327

Sorted by citations

Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I. M. A., Attili, I. B., Azzeh, M., & Shaalan, K. F. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,215
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507
“Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model [27, 28].”
Background
Artificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435
Artificial intelligence in reproductive medicine
Wang, R., Pan, W. D., Jin, L., Li, Y., Geng, Y., Gao, C., Chen, G., Wang, H., Ma, D., & Liao, S. (2019) · Reproduction · doi:10.1530/rep-18-0523 · Cited by 265
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 234
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
Artificial intelligence for brain diseases: A systematic review
Segato, A., Marzullo, A., Calimeri, F., & Momi, E. D. (2020) · APL Bioengineering · doi:10.1063/5.0011697 · Cited by 184
A systematic literature review of speech emotion recognition approaches
Singh, Y. B., & Goel, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.028 · Cited by 156
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 149
Artificial intelligence, physiological genomics, and precision medicine
Williams, A. M., Liu, Y., Regner, K. R., Jotterand, F., Liu, P., & Liang, M. (2018) · Physiological Genomics · doi:10.1152/physiolgenomics.00119.2017 · Cited by 131
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 110
Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance
Davids, J., Manivannan, S., Darzi, A., Giannarou, S., Ashrafian, H., & Marcus, H. J. (2021) · Neurosurgical Review · doi:10.1007/s10143-020-01378-0 · Cited by 106
The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review
Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025) · Antibiotics · doi:10.3390/antibiotics14020134 · Cited by 122
Machine learning for sperm selection
You, J. B., McCallum, C., Wang, Y., Riordon, J., Nosrati, R., & Sinton, D. A. (2021) · Nature Reviews Urology · doi:10.1038/s41585-021-00465-1 · Cited by 104
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning
Ugga, L., Cuocolo, R., Solari, D., Guadagno, E., D’Amico, A., Somma, T., Cappabianca, P., De, M. D. B., Cavallo, L. M., & Brunetti, A. (2019) · Neuroradiology · doi:10.1007/s00234-019-02266-1 · Cited by 97
Machine learning–based preoperative predictive analytics for lumbar spinal stenosis
Siccoli, A., de Wispelaere, M. P., Schröder, M. L., & Staartjes, V. E. (2019) · Neurosurgical FOCUS · doi:10.3171/2019.2.focus18723 · Cited by 93
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 93
Self-citation
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 77
Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI
Cuocolo, R., Ugga, L., Solari, D., Corvino, S., D’Amico, A., Russo, D., Cappabianca, P., Cavallo, L. M., & Elefante, A. (2020) · Neuroradiology · doi:10.1007/s00234-020-02502-z · Cited by 76
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 80
Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results
Stanzione, A., Cuocolo, R., Cocozza, S., Romeo, V., Persico, F., Fusco, F., Longo, N., Brunetti, A., & Imbriaco, M. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.12.025 · Cited by 73
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
Nerve optic segmentation in CT images using a deep learning model and a texture descriptor
Ranjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Safavi, S., Razmjooy, N., Sarshar, N. T., Anari, S., & Bendechache, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00694-w · Cited by 65
Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64
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
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
Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons
Raju, B., Jumah, F., Ashraf, O., Narayan, V., Gupta, G., Sun, H., Hilden, P., & Nanda, A. (2020) · Journal of neurosurgery · doi:10.3171/2020.5.jns201288 · Cited by 58
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 64
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
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 54
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
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 48
A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients
Chen, C., Qin, Y., Chen, H., Zhu, D., Gao, F., & Zhou, X. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01034-1 · Cited by 42
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
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
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
Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas
Danilov, G., Shifrin, M. A., Kotik, K., Ishankulov, T., Orlov, Y. N., Kulikov, A. S., & Aa, P. (2020) · Современные технологии в медицине · doi:10.17691/stm2020.12.5.12 · Cited by 35
Machine learning in deep brain stimulation: A systematic review
Peralta, M., Jannin, P., & Baxter, J. S. H. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102198 · Cited by 34
WITHDRAWN: Voice recognition system using machine learning techniques
Ali, A. T., Abdullah, H. S., & Fadhil, M. N. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.04.075 · Cited by 35
Retracted
GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes
Draelos, R. L., Ezekian, J. E., Zhuang, F., Moya‐Mendez, M. E., Zhang, Z., Rosamilia, M. B., Manivannan, P. K. R., Henao, R., & Landstrom, A. P. (2022) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.121.010326 · Cited by 35
A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy
Ahmed, M. I. B., Alotaibi, S., Rahman, A., Dash, S., Nabil, M., & AlTurki, A. O. (2022) · SN Computer Science · doi:10.1007/s42979-022-01358-9 · Cited by 34
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 36
Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI
Kwong, J. C. C., Wu, J., Malik, S., Khondker, A., Gupta, N. K., Bodnariuc, N., Narayana, K., Malik, M., van der Kwast, T., Johnson, A. E. W., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01088-7 · Cited by 40
Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice
Boaro, A., Kaczmarzyk, J., Kavouridis, V. K., Harary, M., Mammi, M., Dawood, H. Y., Shea, A., Cho, E. Y., Juvekar, P., Noh, T., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-19356-5 · Cited by 35
Self-citation
Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow Efficiency
Sanpablo, A. I. P., Quinzaños-Fresnedo, J., Gutiérrez-Martínez, J., Rodríguez, I. G. L., & Roldán-Valadéz, E. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056370620250403030638 · Cited by 36
Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task
Natheir, S., Christie, S., Yilmaz, R., Winkler-Schwartz, A., Bajunaid, K. M., Sabbagh, A. J., Werthner, P., Fares, J., Azarnoush, H., & Maestro, R. F. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106286 · Cited by 34
Ethical Dilemmas of Using Artificial Intelligence in Medicine
Astărăstoae, V., Rogozea, L. M., Leaşu, F., & Ioan, B. G. (2024) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000001693 · Cited by 36
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 33
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
The Intersection of Artificial Intelligence, Telemedicine, and Neurophysiology
Tavares, D. L., Lopes, A. I., Castro, C., Maia, G. H., Leite, L., & Quintas, M. (2023) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-6684-7164-7.ch006 · Cited by 30
Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility
Huang, J., Shlobin, N. A., DeCuypere, M., & Lam, S. K. (2021) · Neurosurgery · doi:10.1227/neu.0000000000001736 · Cited by 28
Image Processing Approach for Grading IVF Blastocyst: A State-of-the-Art Review and Future Perspective of Deep Learning-Based Models
Isa, I. S., Yusof, U. K., & Zain, M. M. (2023) · Applied Sciences · doi:10.3390/app13021195 · Cited by 25
Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review
Uparela-Reyes, M. J., Villegas-Trujillo, L. M., Cespedes, J., Velásquez-Vera, M., & Rubiano, A. M. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.05.065 · Cited by 26
Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials
Jain, S., Naicker, D., Raj, R., Patel, V., Hu, Y., Srinivasan, K., & Jen, C. (2023) · Diagnostics · doi:10.3390/diagnostics13091563 · Cited by 22
Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values
Chen, C., Qin, Y., Chen, H., Cheng, J., He, B., Wan, Y., Zhu, D., Gao, F., & Zhou, X. (2022) · European Radiology · doi:10.1007/s00330-021-08465-w · Cited by 20
Application of artificial intelligence in gametes and embryos selection
Si, K., Huang, B., & Jin, L. (2023) · Human Fertility · doi:10.1080/14647273.2023.2256980 · Cited by 20
Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques
Vedaei, F., Mashhadi, N., Zabrecky, G. P., Monti, D., Navarreto, E., Hriso, C., Wintering, N., Newberg, A. B., & Mohamed, F. B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1099560 · Cited by 19
Predicting bacteraemia in maternity patients using full blood count parameters: A supervised machine learning algorithm approach
Mooney, C., Eogan, M., Áinle, F. N., Cleary, B., Gallagher, J., O'Loughlin, J. A., & Drew, R. J. (2020) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.13434 · Cited by 18
A Brief History of Machine Learning in Neurosurgery
Schilling, A. T., Shah, P. P., Feghali, J., Jimenez, A. E., & Azad, T. D. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_27 · Cited by 20
Artificial intelligence in neuroradiology: a scoping review of some ethical challenges
Khosravi, P., & Schweitzer, M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1149461 · Cited by 18
Computer-vision based analysis of the neurosurgical scene – A systematic review
Buyck, F., Vandemeulebroucke, J., Ceranka, J., Gestel, F. V., Cornelius, J. F., Duerinck, J., & Bruneau, M. (2023) · Brain and Spine · doi:10.1016/j.bas.2023.102706 · Cited by 18
Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning
Soldozy, S., Farzad, F., Young, S., Yağmurlu, K., Norat, P., Sokolowski, J. D., Park, M. S., Jane, J. A., & Syed, H. R. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.07.104 · Cited by 16
What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation
King, H., Wright, J. M., Treanor, D. E., Williams, B. J., & Randell, R. (2022) · Journal of Medical Internet Research · doi:10.2196/38039 · Cited by 20
Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power
Noor, N. S. E. M., Ibrahim, H., Lah, M. H. C., & Abdullah, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3109780 · Cited by 18
Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning
Miyagawa, T., Sasaki, M., & Yamaura, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240845 · Cited by 16
Applications of Machine Learning in Pediatric Hydrocephalus
Pahwa, B., Bali, O., Goyal, S., & Kedia, S. (2021) · Neurology India · doi:10.4103/0028-3886.332287 · Cited by 15
Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning
Bhatele, K. R., & Bhadauria, S. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13439-1 · Cited by 15
Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke
Velagapudi, L., Mouchtouris, N., Baldassari, M. P., Nauheim, D. O., Khanna, O., Saiegh, F. A., Herial, N. A., Gooch, M. R., Tjoumakaris, S. I., Rosenwasser, R. H., et al. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.105832 · Cited by 15
Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury
Miyagawa, T., Saga, M., Sasaki, M., Shimizu, M., & Yamaura, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278562 · Cited by 15
Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors
Chen, C., Qin, Y., Cheng, J., Gao, F., & Zhou, X. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.701289 · Cited by 14
Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance
Baghdadi, A., Lama, S., Singh, R. S., & Sutherland, G. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-36702-3 · Cited by 14
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I—Introduction and General Principles
Kernbach, J. M., & Staartjes, V. E. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_2 · Cited by 14
Revolutionizing Neurosurgery with GPT-4: A Leap Forward or Ethical Conundrum?
Li, W., Fu, M., Liu, S., & Yu, H. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03240-y · Cited by 14
AI-Driven Automation in a Human-Centered Cyber World
Smith, N. L., Teerawanit, J., & Hamid, O. H. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00551 · Cited by 16
Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas
Sharma, S., Singh, G., & Singh, D. (2019) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.13-7-2018.158419 · Cited by 14
Exploring the Landscape of Machine Learning Applications in Neurosurgery: A Bibliometric Analysis and Narrative Review of Trends and Future Directions
Levy, A. S., Bhatia, S., Merenzon, M. A., Andryski, A. L., Rivera, C. A., Daggubati, L. C., Di, L., Shah, A. H., Komotar, R. J., & Ivan, M. E. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2023.10.042 · Cited by 12
Applications of Artificial Intelligence in Neurosurgery for Improving Outcomes Through Diagnostics, Predictive Tools, and Resident Education
Windermere, S. A., Shah, S., Hey, G. E., McGrath, K. V., & Rahman, M. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2025.123809 · Cited by 13
Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease
Duey, A. H., Rana, A., Siddi, F., Hussein, H., Onnela, J.-P. “., & Smith, T. R. (2023) · Neurosurgery · doi:10.1227/neu.0000000000002474 · Cited by 13
Self-citation
A review of artificial intelligence applications in in vitro fertilization
Zhang, Q. R., Liang, X., & Chen, Z. (2024) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03284-6 · Cited by 12
Survival Prediction After Neurosurgical Resection of Brain Metastases: A Machine Learning Approach
Hulsbergen, A. F. C., Lo, Y. T., Awakimjan, I., Kavouridis, V. K., Phillips, J. G., Smith, T. R., Verhoeff, J. J. C., Yu, K., Broekman, M. L. D., & Arnaout, O. M. (2022) · Neurosurgery · doi:10.1227/neu.0000000000002037 · Cited by 12
Self-citation
Clinical application of the “sellar barrier’s concept” for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis
Villalonga, J. F., Solari, D., Cuocolo, R., Lucia, V. D., Ugga, L., Gragnaniello, C., Pailler, J. I., Cervio, A., Campero, Á., Cavallo, L. M., et al. (2022) · Frontiers in Surgery · doi:10.3389/fsurg.2022.934721 · Cited by 12
Information-Based Medicine in Glioma Patients: A Clinical Perspective
Senders, J. T., Harary, M., Stopa, B. M., Staples, P. C., Broekman, M. L. D., Smith, T. R., Gormley, W. B., & Arnaout, O. M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/8572058 · Cited by 12
Self-citation
Machine Learning–Based Prediction of 6-Month Postoperative Karnofsky Performance Status in Patients with Glioblastoma: Capturing the Real-Life Interaction of Multiple Clinical and Oncologic Factors
Pepa, G. M. D., Caccavella, V. M., Menna, G., Ius, T., Auricchio, A., Chiesa, S., Gaudino, S., Marchese, E., & Olivi, A. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.01.082 · Cited by 11
Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament?
Kim, S. H., Lee, S. H., & Shin, D. A. (2022) · Clinical Spine Surgery A Spine Publication · doi:10.1097/bsd.0000000000001295 · Cited by 11
Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach
Mládek, A., Gerla, V., Skalický, P., Vlasák, A., Zazay, A., Lhotská, L., Beneš, V., Beneš, V., & Bradáč, O. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001838 · Cited by 11
Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review
Agadi, K., Dominari, A., Tebha, S. S., Mohammadi, A., & Zahid, S. (2022) · Journal of Korean Neurosurgical Society · doi:10.3340/jkns.2021.0213 · Cited by 11
Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice
Nahum, U., Refardt, J. C., Chifu, I. O., Fenske, W. K., Faßnacht, M., Szinnai, G., Christ‐Crain, M., & Pfister, M. (2022) · European Journal of Endocrinology · doi:10.1530/eje-22-0368 · Cited by 10
Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions
Velagapudi, L., Saiegh, F. A., Swaminathan, S., Mouchtouris, N., Khanna, O., Sabourin, V. M., Gooch, M. R., Herial, N. A., Tjoumakaris, S. I., Rosenwasser, R. H., et al. (2022) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2022.107547 · Cited by 10
Hyper-parameter tuning for support vector machine using an improved cat swarm optimization algorithm
Abdulraheem, S. A., Aliyu, S., & Abdullahi, F. B. (2023) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2023.1007 · Cited by 9
A Deep Neural Network-Based Model Predicting Peritumoral Edema After Radiosurgery for Meningioma
Kim, K. H., Jung, S., Lee, H.-J., Kwon, H., Choi, S.-W., Koh, H., Youm, J.-Y., & Kim, S. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.04.125 · Cited by 9
Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery
Wu, M., Jiang, X., Du, K., Xu, Y., & Zhang, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-33930-5 · Cited by 9
Machine learning-based clinical prediction modeling -- A practical guide for cliniciansPreprint
Kernbach, J. M., & Staartjes, V. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15069 · Cited by 15
Artificial Intelligence for Prediction of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review
Fernandes, R. T., Fernandes, F. W., Kundu, M., Ramsay, D. S. C., Salih, A. A., Namireddy, S. R., Janković, D., Kalasauskas, D., Ottenhausen, M., Kramer, A., et al. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.09.087 · Cited by 7
Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models
Noor, N. S. E. M., & Ibrahim, H. (2019) · Lecture notes in mechanical engineering · doi:10.1007/978-981-13-9539-0_2 · Cited by 9
Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors
Rivera, C. A., Bhatia, S., Uppalapati, V., Berke, C. N., Merenzon, M. A., Daggubati, L. C., Levy, A. S., Shah, A. H., Komotar, R. J., & Ivan, M. E. (2024) · Neurosurgical FOCUS · doi:10.3171/2024.8.focus24434 · Cited by 6
A bibliometric analysis of perioperative medicine and artificial intelligence
Chan, L. K. M., Mao, B. P., & Zhu, R. (2025) · Journal of Perioperative Practice · doi:10.1177/17504589251320811 · Cited by 6
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer’s Patients
Fu, H., Chen, H., Lin, S., & Xing, G. (2025) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3715014.3722062 · Cited by 10
Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models
Emani, S. S., Swaminathan, A., Grobman, B., Duvall, J. B., López, I., Arnaout, O. M., & Huang, K. T. (2023) · Neurosurgical FOCUS · doi:10.3171/2023.3.focus2352 · Cited by 7
Self-citation
Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus
Staartjes, V. E., Regli, L., & Serra, C. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_1 · Cited by 6
Machine Learning Techniques for Diagnosis of Lower Gastrointestinal Cancer: A Systematic Review
Langarizadeh, M., & Sayadi, M. (2021) · Iranian Red Crescent Medical Journal · doi:10.32592/ircmj.2021.23.7.436 · Cited by 6

Authors: Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O.
Affiliations: Brigham and Women's Hospital (US)
Corresponding author: Arnaout, Omar
ORCID: Senders, Joeky T.
Zaki, Mark M.
Karhade, Aditya V.
Chang, Bliss
Gormley, William B.
Broekman, Marike L.
Smith, Timothy R.
Arnaout, Omar
Venue: Acta Neurochirurgica
Publisher: Springer Science and Business Media LLC
Published: 2017-11-13
Volume: 160
Issue: 1
Pages: 29-38
ISSN: 0001-6268 (ISSN-L)
0942-0940
DOI: 10.1007/s00701-017-3385-8
OpenAlex: W2768899399
Cited by: 178
References: 68
Citations by year: 2026: 10 · 2025: 22 · 2024: 9 · 2023: 28 · 2022: 29 · 2021: 33 · 2020: 22 · 2019: 15 · 2018: 10
FWCI: 0.35
Type: journal article
Language: en
Open access: green
scholarlypublications.universiteitleiden.nl
Licence: www.springer.com/tdm (unspecified)
Locations: Acta Neurochirurgica (publishedVersion)
PubMed (publishedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: machine learning, neurosurgery, epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, hydrocephalus, presurgical planning, intraoperative guidance, neuromonitoring, clinical implementation, model validation, AI ethics
MeSH terms: Machine Learning, Brain, Decision Making, Humans, Prognosis, Spinal Cord, 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

An introduction and overview of machine learning in neurosurgical care

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks