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

Items 101–177 of 177

Predictive models in precision medicine
Berikol, G. B., & Berikol, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00007-0 · Cited by 7
Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records
Shevchenko, E. V., Danilov, G., Usachev, D. Y., Lukshin, V. A., Kotik, K., & Ishankulov, T. (2022) · Burdenko s Journal of Neurosurgery · doi:10.17116/neiro20228606143 · Cited by 8
Variance Reduction in Neurosurgical Practice: The Case for Analytics-Driven Decision Support in the Era of Big Data
Stopa, B. M., Yan, S. C., Dasenbrock, H. H., Kim, D., & Gormley, W. B. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.01.292 · Cited by 5
Self-citation
Letter to the Editor: “Bridging Machine Learning and Clinical Practice in Neurosurgery: Hurdles and Solutions”
Velagapudi, L., D’souza, T., Matias, C. M., & Sharan, A. D. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2019.11.105 · Cited by 5
The intersection of prognostication and code status in patients with severe brain injury
Georgia, M. A. D. (2022) · Journal of Critical Care · doi:10.1016/j.jcrc.2022.153997 · Cited by 4
Deep Brain Stimulation: Mechanisms, Cost-Effectiveness, and Precision Applications Across Neurology and Psychiatry
Costin, H. P., Brehar, F.-M., Corlatescu, A.-D., & Pruna, V. M. (2025) · Biomedicines · doi:10.3390/biomedicines13112691 · Cited by 3
Impacts of artificial intelligence on healthcare business models and outcomes
Kakale, M. M., & Pinelli, N. (2026) · Journal of Health Organization and Management · doi:10.1108/jhom-06-2025-0356 · Cited by 4
MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness
Calandrelli, R., Tran, H. E., Boccia, E., Oliva, E., D’Apolito, G., Boldrini, L., Mattogno, P. P., Chiloiro, S., Gessi, M., Doglietto, F., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00736-9 · Cited by 3
Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas
Chen, C., Hao, L., Bai, B., & Zhang, G. (2025) · BMC Medical Imaging · doi:10.1186/s12880-024-01483-2 · Cited by 3
Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine
Lebina, V. A., Shikhalakhova, O. K., Kokhan, A. A., Rashidov, I. Y., Tazhev, K. A., Filippova, A. V., Myshinskaya, E. P., Symolkina, Y. V., Ibuev, Y. I., Mataeva, A. A., et al. (2025) · Obstetrics Gynecology and Reproduction · doi:10.17749/2313-7347/ob.gyn.rep.2025.591 · Cited by 3
Artificial intelligence in intensive care unit: Current ethical dilemmas
Zarra, F., Rolando, M., Cardona, R. P., & Videtta, W. (2025) · Medico-Legal Journal · doi:10.1177/00258172251332485 · Cited by 3
A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin
Rodríguez-Martínez, E. A., Arzaluz, C. R., Ayala, M. E., Blanco‐Rodríguez, R., Juárez, N., Hernandez-Vargas, E. A., & Aragón, A. (2023) · Cytometry Part A · doi:10.1002/cyto.a.24732 · Cited by 3
A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation
Wu, T.-C., Kim, A., Tsai, C.-T., Gao, A. Y. L., Ghuman, T. S., Paul, A., Castillo, A., Cheng, J. S., Adogwa, O., Ngwenya, L. B., et al. (2024) · Applied Clinical Informatics · doi:10.1055/s-0044-1787119 · Cited by 3
Radiologist vs Machine Learning: A Comparison of Performance in Cancer Imaging
Provenzano, D., Rao, Y. J., Goyal, S., Haji-Momenian, S., Lichtenberger, J. P., & Loew, M. H. (2021) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr52630.2021.9762211 · Cited by 3
WITHDRAWN: Finger veins recognition using machine learning techniques
Ali, A. T., Abdullah, H. S., & Fadhil, M. N. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.04.076 · Cited by 3
Retracted
Predictive Modeling of Nonfunctioning Giant Pituitary Neuroendocrine Tumor Resection: A Multi-Planar Perspective
Patel, B. K., Zohdy, Y. M., Lohana, S., Tariciotti, L., Rodas, A., Alawieh, A. M., Jahangiri, A., Faraj, R. R., Maldonado, J., Uribe-Pacheco, R., et al. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2024.123653 · Cited by 2
Machine Learning: A Novel Approach for Predicting Visual Outcomes and Factors Affecting it in Patients with Pituitary Adenomas
Shah, S. P., Ranjith, G., Narendran, M., Patel, B. K., Gowda, A., George, T., Easwer, H. V., & Nair, P. (2025) · Neurology India · doi:10.4103/neurol-india.neurol-india-d-24-00350 · Cited by 2
Are thinking machines breaking new frontiers in neuro-oncology? A narrative review on the emerging role of machine learning in neuro-oncological practice
Hussain, M., Shabbir, A., Bakhshi, S. K., & Shamim, M. S. (2021) · Asian Journal of Neurosurgery · doi:10.4103/ajns.ajns_265_20 · Cited by 2
Applications of Machine Learning Model for Prediction of Outcomes in Primary Pontine Hemorrhage
Yindeedej, V., Setprapha, C., Komarapaj, C., Osirichaivait, K., Nimmannitya, P., & Noiphithak, R. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2023.05.014 · Cited by 2
Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma
Kavak, E. E., Erdat, E. C., Derin, Z. A., Dilli, İ., Tolunay, P. K., Öksüzoğlu, B. Ç., & Akbulut, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-06591-9 · Cited by 1
Aprendizaje Automático en Aplicaciones Fisioterapéuticas
González-Islas, J.-C. (2019) · PÄDI Boletín Científico de Ciencias Básicas e Ingenierías del ICBI · doi:10.29057/icbi.v7iespecial.4473 · Cited by 2
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V—A Practical Approach to Regression Problems
Staartjes, V. E., & Kernbach, J. M. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_6 · Cited by 3
Machine Learning and Epilepsy
Ghosh, S., & Dasgupta, R. (2022) · Springer eBooks · doi:10.1007/978-981-16-8881-2_15 · Cited by 2
Systematic Literature Review on Convolutional Neural Networks for Vascular Surgeries
Gamboa-Cruzado, J., Rojas-Morales, M., López-Goycochea, J., Tinoco, E. E. C., Paucar-Carlos, G., & Damián, A. P. S. (2022) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v18i12.31535 · Cited by 2
Commentary: Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model
Zaki, M. M. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa591 · Cited by 2
Self-citation
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV—A Practical Approach to Binary Classification Problems
Staartjes, V. E., & Kernbach, J. M. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_5 · Cited by 1
Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage
Menon, G. R., Hareesha, K. S., Gajula, K., Kumar, S., & Hegde, A. (2022) · Journal of Stroke Medicine · doi:10.1177/25166085221127861 · Cited by 1
Diagnosis and Prognosis: Literature Review on Prediction of Epilepsy using Machine Learning Techniques
Ahsan, A., & Siddiqi, S. (2023) · International Journal of Computer Applications · doi:10.5120/ijca2023922840 · Cited by 1
ARTIFICIAL INTELLIGENCE IN SCOLIOSIS DIAGNOSIS: A COMPARATIVE STUDY BETWEEN CHATGPT AND SURGEONS
de Oliveira, L. S. R., de Souza, R. C., CESAR, A. E. M., Motter, B. V., Oliveira, W. H. D. A., Foizer, G. A., Pereira, G. D. A. V., & Rodrigues, L. M. R. (2025) · Coluna/Columna · doi:10.1590/s1808-185120252402293157 · Cited by 1
Role of Artificial Intelligence in Early Diagnosis and Risk Stratification of Arterial Aneurysms
Rajale, P., Meshram, N., Joshi, S., Muleva, G., Vinchurkar, K., & Borse, L. (2026) · Indian Journal of Vascular and Endovascular Surgery · doi:10.4103/ijves.ijves_161_25 · Cited by 1
Improving Outcomes: Big Data and Predictive Analytics
Karhade, A. V., Gormley, W. B., & Smith, T. R. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812898-5.00016-3 · Cited by 1
Self-citation
AIM in Obstetrics and Gynecology
Muthu, S., Nabi, F., & Nabi, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_306-1 · Cited by 1
Fair Transmission of Individual Signals and Formation of Mainstream Information: Evidence from Herd Behaviours in Emergencies
Wu, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8229956 · Cited by 1
Artificial Intelligence: A Tool to Help Cancer Diagnosis, Prognosis, and Treatment
Saghazadeh, A., & Rezaei, N. (2023) · Springer eBooks · doi:10.1007/978-3-030-80962-1_309-1 · Cited by 1
Survey of neuroimaging in neurological surgery, current state, and emerging research
Narasimhan, S., & González, H. F. J. (2023) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2023.1022680 · Cited by 1
Application of Machine Learning Methods in NPH
Mládek, A., Gerla, V., Zazay, A., & Bradáč, O. (2023) · Springer eBooks · doi:10.1007/978-3-031-36522-5_19 · Cited by 1
Development of an artificial intelligence-based convolutional neural network for sellar barrier classification using magnetic resonance imaging
Villar, L. E. D. B., Baldoncini, M., Campero, Á., & Demichelis, M. E. (2025) · Surgical Neurology International · doi:10.25259/sni_303_2025 · Cited by 1
AIM in Obstetrics and Gynecology
Muthu, S., Nabi, F., & Nabi, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_306 · Cited by 1
Commentary: Reporting Guidelines for Studies on Artificial Intelligence: What Neurosurgeons Should Know
Shlobin, N. A., & Moher, D. (2021) · Neurosurgery · doi:10.1093/neuros/nyab331 · Cited by 1
Letter: Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning’s ability to provide diagnoses and treatment plans for example neuro-oncology cases
Zarra, F., Gandhi, D. N., Karki, A., & Chaurasia, B. K. (2024) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2024.108270 · Cited by 1
Artificial intelligence for management of patients with intracranial neoplasms
Mammi, M., Boaro, A., Kavouridis, V. K., Hulsbergen, A. F. C., Senders, J. T., Gormley, W. B., Smith, T. R., & Arnaout, O. M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00009-4
Self-citation
A Supervised Machine learning-powered tool: intraoperative CSF leak predictor in endoscopic transsphenoidal surgery for pituitary adenomas.Preprint
Tariciotti, L., Fiore, G., Carrabba, G. G., Bertani, G., Ferrante, E., Caccavella, V. M., Mattogno, P. P., Mantovani, G., & Locatelli, M. (2020) · doi:10.22541/au.160451286.60501310/v1
Brief Review of Machine Learning in Neurosurgery
Roka, Y. B. (2018) · Nepal Journal of Neuroscience · doi:10.3126/njn.v15i3.23268
Predictive analytics and multimodal imaging-guided decision-making in neurosurgery
Moreau, J. T. (2020) · doi:10.82308/9754
AIM in Neurology
Das, D., & Mahanta, L. B. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_189
Internet of Nanothings (IoNT) and Machine Learning (ML) – Innovations in drug discovery and Healthcare system
Prabhu, R. S., Sabithaananthi, D., Umamaheswari, D., & Rajasoundarya, S. (2022) · International Journal of Frontiers in Science and Technology Research · doi:10.53294/ijfstr.2022.2.1.0021 · Cited by 1
Educação financeira por meio de uma plataforma interativa de investimento: a aplicabilidade da inteligência artificial (IA)
Freitas, D. D. C., & Reis, D. L. (2022) · Brazilian Journal of Development · doi:10.34117/bjdv8n11-320
References
Beyala, L. (2022) · Wiley eBooks · doi:10.1002/9781394192342.refs
Combating COVID-19 by employing machine learning predictions and projections
Malhotra, A. G., Borkar, P., Chowdhary, R., & Singh, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85955-4.00003-x
A computational model to derive certain human cognitive mind functions.Preprint
Dundas, J., & Ling, M. H. (2023) · doi:10.31219/osf.io/56gkp
A Computational Approach to Understand the Human Thought Process
Dundas, J., & Ling, M. H. (2023) · International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) · doi:10.1109/ice3is59323.2023.10335478
The effect of machine learning and artificial intelligence on the use of robots in neurosurgery
Bhattacharyya, D. (2019) · Advances in Clinical Neuroscience & Rehabilitation · doi:10.47795/ojhn3816
Livin’ on a Prayer: The Intersection of Prognostication, Code Status, and Palliative Care
Georgia, M. A. D. (2024) · Springer eBooks · doi:10.1007/978-3-031-69686-2_16
The Neurosurgical Uncertainty Index: Self-Doubting AI for rare or unexpected surgical complicationsPreprint
Thiong’o, G. M., & Ogundokun, A. (2025) · medRxiv · doi:10.1101/2025.05.01.25326833
Automated tumor localization in IGRT system via deep reinforcement learning and genetic algorithm
Saeed, U., Khuhro, M. A., Kumar, K., Laghari, A. A., Shaikh, A. A., Khan, U. A., & Solangi, S. (2025) · Human-Intelligent Systems Integration · doi:10.1007/s42454-025-00078-8
Artificial Intelligence: A Tool to Help Cancer Diagnosis, Prognosis, and Treatment
Saghazadeh, A., & Rezaei, N. (2025) · Springer eBooks · doi:10.1007/978-3-032-00763-6_309
Future Directions in Cranial Approaches
Palmiero, H. D. O. M., & Figueiredo, E. G. (2025) · Springer eBooks · doi:10.1007/978-3-032-04664-2_21
Computer Science: The Computer as a Neurosurgical Tool
Koo, A. B., & King, J. D. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009597852.015
Highly Cited Artificial Intelligence Research Studies Published in Neurosurgical Journals: A Bibliometric Analysis
Maghrabi, Y., Jamjoom, A. B., Algahtani, A. Y., Alshareef, O. H., Jamjoom, O. M., & Alzahrani, M. T. (2025) · Cureus · doi:10.7759/cureus.98191
Additional file 4 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544760
Additional file 1 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037896
Additional file 1 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544751
Additional file 3 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544757
Additional file 4 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037905
Additional file 2 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037899
Additional file 2 of Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · Figshare · doi:10.6084/m9.figshare.14544754
Additional file 3 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037902
Artificial Intelligence Perspective on MRI-Driven Computer-Aided Diagnosis and Prognosis of the Brain: A Systematic Review
Qasim, S., Pradhan, N., & Shweta (2025) · BIO Web of Conferences · doi:10.1051/bioconf/202520401024
Clinical differences and prognostic factors for unfavourable outcome in community-acquired versus health care-associated intracranial empyema and abscessPreprint
Hellwege, R. S., Gönel, M., Sebök, M., Sarnthein, J., Weller, D., Brugger, S. D., & Brandi, G. (2026) · Research Square · doi:10.21203/rs.3.rs-8357670/v1
Enhancing Disease Management Through Machine Learning: Accurate Prediction and Early Detection for Improved Patient Outcomes
Mahindre, A. A., & Kondekar, S. A. (2025) · International Conference on Future Technologies (ICFT) · doi:10.1109/icft66708.2025.11336423
Data driven methods to support decision making in Deep Brain Stimulation for Parkinson’s disease
Peralta, M. (2020) · theses.fr (ABES) · doi:10.70675/59262743z5225z46d3z984ez3f9b0a19c4a7
Yapay zeka ve kadın sağlığı
Ecemiş, E., Aydemir, G., & Uğurlu, M. (2026) · Sağlık akademisyenleri dergisi/Sağlık akademisyenler dergisi · doi:10.52880/sagakaderg.1904844
Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review
Kamran, M., Majeed, A., Haque, A. S. M. R., & Abdullah, J. Y. (2026) · Health Science Reports · doi:10.1002/hsr2.72884
Predictive Models and Artificial Intelligence for Risk Stratification in Emergency Abdominal Surgery: A Contemporary Review
Cosma, C., Butiurcă, V. O., Botoncea, M., Molnar, D., & Molnar, C.-D. (2026) · Emergency Care and Medicine · doi:10.3390/ecm3030024
Machine Learning-Driven Predictive Analytics for Early Detection of Diabetes: A Comparative Study of XGBoost and Neural Networks
Mahindre, A. A., & Kondekar, S. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31216-7_45
Multidisciplinary Approaches to the Management of Spinal Care: A Narrative Review
AbdelAlim, Y. A., Sylvester, Z. T., Sulaj, E., Riahi, A., Neitzel, I., Barsch, J., & Goldstein, I. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02596-1
Machine learning-based voice recognition system
Parshotam, & Sharma, S. (2026) · AIP conference proceedings · doi:10.1063/5.0285817

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