ArticleScientific reports2023
Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Prognostic value of machine learning for brain computed tomography as a predictor of neurologic outcomes after cardiac arrest: a systematic review and meta-analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Pooled it
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Hounsfield unit and gray-white matter ratio estimates in pediatric normal controls used in studies evaluating head computed tomography.Neuroimage. Reports · 2026Article
- Article
- Gray-white matter ratio in pediatric and adult cardiopulmonary arrest studies: a rapid review.Resuscitation plus · 2026Review
- Improving wildlife track classification through human-in-the-loop method and explainable AI.Scientific reports · 2026Article
- Multimodal contrastive prognostication framework for early neurological outcome prediction in post-cardiac arrest patients.Scientific reports · 2026Article
- Quantitative assessment of apparent diffusion coefficient for neurological outcome prediction in status epilepticus: a pilot study.Scientific reports · 2026Article
- Early prediction of in-hospital acute brain injury following cardiac arrest using machine learning: development and external validation.Frontiers in neurology · 2026Article
- Artificial Intelligence in Cardiopulmonary Resuscitation.Medicina (Kaunas, Lithuania) · 2025Review
- Multimodal ensemble machine learning predicts neurological outcome within three hours after out of hospital cardiac arrest.Scientific reports · 2025Article
- Clinical heterogeneity and phenotyping of post cardiac arrest brain injury: one size may not fit all.Intensive care medicine · 2025Review
- Radiomics for Predicting the Development of Brain Edema from Normal-Appearing Early Brain-CT After Cardiac Arrest and Return of Spontaneous Circulation.Diagnostics (Basel, Switzerland) · 2025Article
- Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation.Annals of medicine and surgery (2012) · 2024Review
- Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.Resuscitation · 2024Article
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic- ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.
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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.