SynthesisNeurosurgical review2025
Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants.
Synthesis in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 11 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence for predicting pituitary neuroendocrine tumor consistency: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Prediction models for functional outcomes in prolonged disorders of consciousness: a systematic review and meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Machine learning models for predicting vasospasm following ruptured intracranial aneurysms: a systematic review and meta-analysis.Acta neurochirurgica · 2025Pooled it
- The roles of radiomics and deep learning for automatic detection, stability assessment, and rupture risk prediction in intracranial aneurysms: a systematic review.European journal of medical research · 2025Pooled it
- Can artificial intelligence accurately predict the risk of hematoma expansion in intracerebral hemorrhage? A systematic review and Meta-analysis of 7,665 patients.Neurosurgical review · 2025Pooled it
- The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Machine learning-based models and radiomics: can they be reliable predictors for meningioma recurrence? A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Artificial intelligence algorithms for differentiating pseudoprogression from true progression in high-grade gliomas: A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.BMC endocrine disorders · 2025Pooled it
- Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants.Neurosurgical review · 2025Pooled it
- Predicting time-to-event outcomes in critically ill patients with intracerebral hemorrhage using machine learning.The Journal of international medical research · 2026Article
- Evaluating AI chatbots in neurological function test interpretation for brain tumor surgery.Neurosurgical review · 2026Article
- Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions.Bioengineering (Basel, Switzerland) · 2025Review
- Construction of a nomogram model for predicting delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients.Scientific reports · 2025Article
- Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.BMC infectious diseases · 2025Article
- Machine learning model MASKRCNN_RESNET50_FPN in image segmentation for the detection of the long head of biceps in arthroscopy images.Science progressArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63-0.89], specificity of 0.78[95% CI: 0.68-0.85], positive DLR of 3.54 [95% CI: 2.22-5.64] and the negative DLR of 0.28 [95% CI: 0.15-0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66-35.28], the diagnostic score of 2.55 [95% CI: 1.54-3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.