Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.
Background: Predicting hematoma progression in traumatic brain injury (TBI) is crucial for timely interventions and effective clinical management, as unchecked hematoma growth can lead to rapid neurological deterioration, increased intracranial pressure, and poor patient outcomes. Accurate risk asse...
| Publicado en: | Neuroradiology Vol. 67; no. 7; pp. 1733 - 1750 |
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| Autores principales: | , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187625136&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187625136 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2025 vid: 67 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187625136 185298100 187625136 187625136 10.1007/s00234-025-03657-3 187625136 ppf: 1733 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis. aug: au: Mohammadzadeh, Ibrahim Hajikarimloo, Bardia Eini, Pooya Niroomand, Behnaz Mohammadzadeh, Shahin Habibi, Mohammad Amin Babak, Zohre Masoumi Shahr-e Aliaghaei, Abbas affil: https://ror.org/034m2b326 Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran sug: subj: Brain Injuries Complications Hematoma Physiopathology Disease Progression Risk Factors Machine Learning Algorithms Prediction Algorithms Prediction Models Risk Assessment Human Systematic Review Meta Analysis Embase PubMed Descriptive Statistics Sensitivity and Specificity Decision Support Systems, Clinical Diagnosis, Computer Assisted Odds Ratio Confidence Intervals Artificial Intelligence Predictive Value of Tests Cerebral Hemorrhage Risk Factors Hematoma, Epidural, Cranial Risk Factors Hematoma, Subdural Risk Factors Deep Learning Decision Making, Clinical ab: Background: Predicting hematoma progression in traumatic brain injury (TBI) is crucial for timely interventions and effective clinical management, as unchecked hematoma growth can lead to rapid neurological deterioration, increased intracranial pressure, and poor patient outcomes. Accurate risk assessment enables proactive therapeutic strategies, minimizing secondary brain damage and improving survival rates. Methods: This study evaluated to assess the performance of artificial intelligence (AI) algorithms, including machine learning (ML) and deep learning (DL), in forecasting risk of hematoma progression. Comprehensive searches across Embase, Scopus, Web of Science and PubMed identified relevant studies, with data extracted on algorithm metrics such as sensitivity, specificity, and area under the curve (AUC). Results: 1,240 studies screened, five out of them met the inclusion criteria, evaluating various AI models. The meta-analysis revealed a pooled sensitivity and specificity was 0.76 [95% CI: 0.67–0.83], 0.84 [95% CI: 0.78–0.89], positive and negative likelihood ratio was 4.82 [95% CI: 3.51–6.61] 0.29 [95% CI: 0.21–0.39], diagnostic score was 2.82 [95% CI: 2.33–3.32], diagnostic odds ratio was16.85 [95% CI: 10.29–27.59] and an AUC of 0.88 [95% CI: 0.85–0.90]. Among the evaluated algorithms, XGBoost has the best predictive performance with an accuracy of 91%. Integrating radiomics and clinical features in these models considerably improved the predictive outcomes. Conclusion: The current results demonstrated the potential of AI-based models to improve hematoma progression prediction for TBI patients, thereby supporting more effective clinical decision-making. Further research should aim to standardize datasets and diversify patient populations to improve model applicability and reliability. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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