SynthesisBiomedical engineering online2023
Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.
Synthesis in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis.Neurosurgical review · 2026Pooled it
- Global diagnostic accuracy and prognostic value of non-contrast CT in acute traumatic brain injury: a systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- Towards Explainability in Deep Learning for Detection of Five Major Intracranial Hemorrhage Subtypes on Head CT Using Multi-window DICOM Imaging and Patient-Level Cross-Validation.Journal of imaging informatics in medicine · 2026Article
- Diagnostic Accuracy and Time Efficiency of Artificial Intelligence for Intracranial Hemorrhage Detection on Emergency Brain CT.Diagnostics (Basel, Switzerland) · 2026Article
- AI-assisted versus fully automated volumetry for hemorrhage quantification in spontaneous ICH with ventricular extension: A multi-center study.European journal of radiology open · 2026Article
- Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography.The Cochrane database of systematic reviews · 2026Article
- Automated volume estimate of the brain's lateral ventricles reveals a persistent association with cognitive performance among stroke survivors.Cerebral circulation - cognition and behavior · 2026Article
- High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach.Scientific reports · 2025Article
- Standalone AI Versus AI-Assisted Radiologists in Emergency ICH Detection: A Prospective, Multicenter Diagnostic Accuracy Study.Journal of clinical medicine · 2025Article
- Article
- Real-Life Performance of a Commercially Available AI Tool for Post-Traumatic Intracranial Hemorrhage Detection on CT Scans: A Supportive Tool.Journal of clinical medicine · 2025Article
- Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization.F1000Research · 2025Article
- Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans.La Radiologia medica · 2024Article
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.Brain sciences · 2024Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans.
methodsUntil May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used.
resultsAt last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88-0.943, I
conclusionThis meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).
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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.