ArticleNeuroradiology2025
Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Review
- 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
- Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review.Journal of clinical medicine · 2026Review
- Diagnostic performance and confidence of an optimized deep-learning algorithm for the detection of intracranial hemorrhages.Insights into imaging · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
purposeIntracranial hemorrhage (ICH) requires urgent treatment, and accurate and timely diagnosis is essential for improving outcomes. This pivotal clinical trial aimed to validate a deep learning algorithm for ICH detection and assess its clinical utility through a reader performance test.
methodsRetrospective CT scans from patients with and without ICH were collected from a tertiary hospital. Two experts evaluated all scans, with a third expert reviewing disagreements for the final diagnosis. We analyzed the performance of the deep learning algorithm, JLK-ICH, for all cases and ICH subtypes. Additional external validation was performed using a multi-ethnic U.S. DATASET: A reader performance study included six non-expert readers who evaluated 800 CT scans, with and without JLK-ICH assistance, following a washout period. ICH presence and five-point scale confidence level for decisions were rated.
resultsA total of 1,370 CT scans were evaluated. The deep learning model showed 98.7% sensitivity (95% confidence interval [CI] 97.8-99.3%), 88.5% specificity (95% CI, 83.6-92.3%), and an area under the receiver operating characteristic curve (AUROC) of 0.936 (95% CI, 0.915-0.957). The model maintained high accuracy across all ICH subtypes, and additional external validation confirmed these results. In the reader performance study, AUROC with JLK-ICH assistance (0.967 [0.953-0.981]) surpassed that without assistance (0.953 [0.938-0.957]; P = 0.009). JLK-ICH particularly improved performance when readers were highly uncertain.
conclusionThe JLK-ICH algorithm demonstrated high accuracy in detecting all ICH subtypes. Non-expert readers significantly improved diagnostic accuracy for brain CT scans with deep learning assistance.
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