ArticleTranslational stroke research2021
Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage.
Article in Translational stroke research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines.Neurocritical care · 2026Pooled it
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis.International journal of clinical practice · 2022Pooled it
- Review
- Article
- Association between the remnant cholesterol and the risk of new-onset chronic diseases: evidence from CHARLS.Diabetology & metabolic syndrome · 2026Article
- Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models.Computer methods and programs in biomedicine · 2025Article
- The role of imaging in predicting 3-month prognosis of primary intracerebral hemorrhage: a single-center, prospective observational study in tertiary care hospital.Quantitative imaging in medicine and surgery · 2025Article
- Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage.BMC medical imaging · 2025Article
- Early NCCT imaging signs for prognostication in intracerebral hemorrhage: a retrospective cohort study with long follow up results.BMC neurology · 2025Article
- Machine Learning-Based Models for Intracerebral Hemorrhage In-Hospital Mortality Prediction.Journal of the American Heart Association · 2025Article
- Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT.Radiology advances · 2025Article
- Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.Journal of the American Heart Association · 2024Article
- Anisocoria After Direct Light Stimulus is Associated with Poor Outcomes Following Acute Brain Injury.Neurocritical care · 2024Article
- Article
- An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.BMC medical imaging · 2024Article
- CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.European radiology · 2024Article
- Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.Brain sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning-based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.
Indexed as
Identifiers
What OpenQuestion holds
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.