{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T02:11:54Z","timestamp":1788487914987,"version":"build-2803163510"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,11]]},"DOI":"10.7759\/cureus.96600","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T20:19:18Z","timestamp":1762892358000},"source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence in Traumatic Brain Injury: A Systematic Review of Prognostic, Diagnostic, and Monitoring Applications"],"prefix":"10.7759","author":[{"given":"Anas E","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rayan M","family":"Alyami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatimah H","family":"Al Ghazwi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renad H","family":"Hamzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nawa K","family":"Alshammari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fawziah M","family":"Jali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah A","family":"Al Alduwayh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thikra M","family":"Almujami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah S","family":"Alamri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamal A","family":"Sabban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghadi F","family":"Alsum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare12222266","article-title":"Traumatic brain injury as a public health issue: epidemiology, prognostic factors and useful data from forensic practice","volume":"12","author":"Karaboue MA","year":"2024","unstructured":"Karaboue MA, Ministeri F, Sessa F, et al.. Traumatic brain injury as a public health issue: epidemiology, prognostic factors and useful data from forensic practice. Healthcare (Basel). 2024, 12:2266. 10.3390\/healthcare12222266","journal-title":"Healthcare (Basel)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/10790268.2025.2571822","article-title":"Risk factors for complications in traumatic spinal cord injury: A retrospective analysis of a cohort of patients identified from administrative data","author":"Bond M","year":"2025","unstructured":"Bond M, Beresford A, Noonan VK, et al.. Risk factors for complications in traumatic spinal cord injury: A retrospective analysis of a cohort of patients identified from administrative data. J Spinal Cord Med. 2025, 1-12. 10.1080\/10790268.2025.2571822","journal-title":"J Spinal Cord Med"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1080\/02699052.2025.2551162","article-title":"What traditional neuropsychological assessment got wrong about mild traumatic brain injury. I: historical perspective, contemporary neuroimaging overview and neuropathology update","author":"Bigler ED","year":"2025","unstructured":"Bigler ED, Allder S, Dunkley BT, Victoroff J. What traditional neuropsychological assessment got wrong about mild traumatic brain injury. I: historical perspective, contemporary neuroimaging overview and neuropathology update. Brain Inj. 2025, 1-23. 10.1080\/02699052.2025.2551162","journal-title":"Brain Inj"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1186\/s40001-025-02477-8","article-title":"Are the same parameters measured at admission and in the ICU comparable in their predictive values for complication and mortality in severely injured patients?","volume":"30","author":"Gr\u00f6bli L","year":"2025","unstructured":"Gr\u00f6bli L, Kalbas Y, Kessler F, et al.. Are the same parameters measured at admission and in the ICU comparable in their predictive values for complication and mortality in severely injured patients?. Eur J Med Res. 2025, 30:228. 10.1186\/s40001-025-02477-8","journal-title":"Eur J Med Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"Pinto-Coelho L","year":"2023","unstructured":"Pinto-Coelho L. How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications. Bioengineering (Basel). 2023, 10:1435. 10.3390\/bioengineering10121435","journal-title":"Bioengineering (Basel)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15050648","article-title":"The value of clinical decision support in healthcare: a focus on screening and early detection","volume":"15","author":"Sch\u00e4fer H","year":"2025","unstructured":"Sch\u00e4fer H, Lajmi N, Valente P, et al.. The value of clinical decision support in healthcare: a focus on screening and early detection. Diagnostics (Basel). 2025, 15:648. 10.3390\/diagnostics15050648","journal-title":"Diagnostics (Basel)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.59797","article-title":"Artificial intelligence in the intensive care unit: current evidence on an inevitable future tool","volume":"16","author":"Suresh V","year":"2024","unstructured":"Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial intelligence in the intensive care unit: current evidence on an inevitable future tool. Cureus. 2024, 16:e59797. 10.7759\/cureus.59797","journal-title":"Cureus"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10238-025-01590-6","article-title":"Advances in risk prediction models for cancer-related cognitive impairment","volume":"25","author":"Duan R","year":"2025","unstructured":"Duan R, Wen Z, Zhang T, Liu J, Feng T, Ren T. Advances in risk prediction models for cancer-related cognitive impairment. Clin Exp Med. 2025, 25:74. 10.1007\/s10238-025-01590-6","journal-title":"Clin Exp Med"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2021.02.003","article-title":"Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement","volume":"134","author":"Page MJ","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, et al.. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol. 2021, 134:103-12. 10.1016\/j.jclinepi.2021.02.003","journal-title":"J Clin Epidemiol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-7-16","article-title":"Utilization of the PICO framework to improve searching PubMed for clinical questions","volume":"7","author":"Schardt C","year":"2007","unstructured":"Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007, 7:16. 10.1186\/1472-6947-7-16","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff RF","year":"2019","unstructured":"Wolff RF, Moons KG, Riley RD, et al.. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019, 170:51-8. 10.7326\/M18-1376","journal-title":"Ann Intern Med"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-46945-9","article-title":"Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans","volume":"13","author":"Brossard C","year":"2023","unstructured":"Brossard C, Gr\u00e8ze J, de Bussch\u00e8re JA, et al.. Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans. Sci Rep. 2023, 13:20155. 10.1038\/s41598-023-46945-9","journal-title":"Sci Rep"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/jpm13091401","article-title":"Development and validation of an XGBoost-algorithm-powered survival model for predicting in-hospital mortality based on 545,388 isolated severe traumatic brain injury patients from the TQIP database","volume":"13","author":"Cao Y","year":"2023","unstructured":"Cao Y, Forssten MP, Sarani B, Montgomery S, Mohseni S. Development and validation of an XGBoost-algorithm-powered survival model for predicting in-hospital mortality based on 545,388 isolated severe traumatic brain injury patients from the TQIP database. J Pers Med. 2023, 13:1401. 10.3390\/jpm13091401","journal-title":"J Pers Med"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s41016-021-00242-4","article-title":"Death after discharge: prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy","volume":"7","author":"Cui W","year":"2021","unstructured":"Cui W, Ge S, Shi Y, et al.. Death after discharge: prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy. Chin Neurosurg J. 2021, 7:24. 10.1186\/s41016-021-00242-4","journal-title":"Chin Neurosurg J"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2020.7018","article-title":"Predicting post-concussion symptom recovery in adolescents using a novel artificial intelligence","volume":"38","author":"Fleck DE","year":"2021","unstructured":"Fleck DE, Ernest N, Asch R, et al.. Predicting post-concussion symptom recovery in adolescents using a novel artificial intelligence. J Neurotrauma. 2021, 38:830-6. 10.1089\/neu.2020.7018","journal-title":"J Neurotrauma"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2019.6705","article-title":"Unsupervised machine learning reveals novel traumatic brain injury patient phenotypes with distinct acute injury profiles and long-term outcomes","volume":"37","author":"Folweiler KA","year":"2020","unstructured":"Folweiler KA, Sandsmark DK, Diaz-Arrastia R, Cohen AS, Masino AJ. Unsupervised machine learning reveals novel traumatic brain injury patient phenotypes with distinct acute injury profiles and long-term outcomes. J Neurotrauma. 2020, 37:1431-44. 10.1089\/neu.2019.6705","journal-title":"J Neurotrauma"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13152605","article-title":"Machine learning approach for the prediction of in-hospital mortality in traumatic brain injury using bio-clinical markers at presentation to the emergency department","volume":"13","author":"Mekkodathil A","year":"2023","unstructured":"Mekkodathil A, El-Menyar A, Naduvilekandy M, Rizoli S, Al-Thani H. Machine learning approach for the prediction of in-hospital mortality in traumatic brain injury using bio-clinical markers at presentation to the emergency department. Diagnostics (Basel). 2023, 13:2605. 10.3390\/diagnostics13152605","journal-title":"Diagnostics (Basel)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01953-z","article-title":"An interpretable neural network for outcome prediction in traumatic brain injury","volume":"22","author":"Minoccheri C","year":"2022","unstructured":"Minoccheri C, Williamson CA, Hemmila M, Ward K, Stein EB, Gryak J, Najarian K. An interpretable neural network for outcome prediction in traumatic brain injury. BMC Med Inform Decis Mak. 2022, 22:203. 10.1186\/s12911-022-01953-z","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref19","article-title":"Recurrent neural network based time-series modeling for long-term prognosis following acute traumatic brain injury","volume":"2021","author":"Nayebi A","year":"2022","unstructured":"Nayebi A, Tipirneni S, Foreman B, Ratcliff J, Reddy CK, Subbian V. Recurrent neural network based time-series modeling for long-term prognosis following acute traumatic brain injury. AMIA Annu Symp Proc. 2022, 2021:900-9.","journal-title":"AMIA Annu Symp Proc"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.4266\/acc.2021.00486","article-title":"A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months","volume":"37","author":"Nourelahi M","year":"2022","unstructured":"Nourelahi M, Dadboud F, Khalili H, Niakan A, Parsaei H. A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months. Acute Crit Care. 2022, 37:45-52. 10.4266\/acc.2021.00486","journal-title":"Acute Crit Care"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53889-6","article-title":"Machine learning-based dynamic mortality prediction after traumatic brain injury","volume":"9","author":"Raj R","year":"2019","unstructured":"Raj R, Luostarinen T, Pursiainen E, et al.. Machine learning-based dynamic mortality prediction after traumatic brain injury. Sci Rep. 2019, 9:17672. 10.1038\/s41598-019-53889-6","journal-title":"Sci Rep"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0290721","article-title":"Applicability of machine learning technique in the screening of patients with mild traumatic brain injury","volume":"18","author":"Terabe ML","year":"2023","unstructured":"Terabe ML, Massago M, Iora PH, et al.. Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. PLoS One. 2023, 18:e0290721. 10.1371\/journal.pone.0290721","journal-title":"PLoS One"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12050612","article-title":"A computer-assisted system for early mortality risk prediction in patients with traumatic brain injury using artificial intelligence algorithms in emergency room triage","volume":"12","author":"Tu KC","year":"2022","unstructured":"Tu KC, Eric Nyam TT, Wang CC, et al.. A computer-assisted system for early mortality risk prediction in patients with traumatic brain injury using artificial intelligence algorithms in emergency room triage. Brain Sci. 2022, 12:612. 10.3390\/brainsci12050612","journal-title":"Brain Sci"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/435097-artificial-intelligence-in-traumatic-brain-injury-a-systematic-review-of-prognostic-diagnostic-and-monitoring-applications","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T20:19:19Z","timestamp":1762892359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/435097-artificial-intelligence-in-traumatic-brain-injury-a-systematic-review-of-prognostic-diagnostic-and-monitoring-applications"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,11]]},"references-count":23,"URL":"https:\/\/doi.org\/10.7759\/cureus.96600","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,11]]}}}