ArticleCureus2022
Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage.
Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06018545 (AI Assisted Reader Evaluation in Acute CT Head Interpretation), which is not on this map. Cited by 6 papers.
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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.
AI Assisted Reader Evaluation in Acute CT Head Interpretation
Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Navigating transcranial mr guided focused ultrasound complexities with machine learning: Overcoming obstacles and expanding therapeutic horizons.Neurosurgical review · 2026Review
- Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study.BMJ digital health & AI · 2026Article
- Real-world performance evaluation of a commercial deep learning model for intracranial hemorrhage detection.NPJ digital medicine · 2025Article
- Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance.Neuroradiology · 2025Article
- Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.BMJ open · 2024Article
Corrections and comments
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Authors and funding
12 authors at 8 institutions in 1 country.
Funding
Abstract
backgroundIntracranial hemorrhage (ICH) requires emergent medical treatment for positive outcomes. While previous artificial intelligence (AI) solutions achieved rapid diagnostics, none were shown to improve the performance of radiologists in detecting ICHs. Here, we show that the Caire ICH artificial intelligence system enhances a radiologist's ICH diagnosis performance.
methodsA dataset of non-contrast-enhanced axial cranial computed tomography (CT) scans (n=532) were labeled for the presence or absence of an ICH. If an ICH was detected, its ICH subtype was identified. After a washout period, the three radiologists reviewed the same dataset with the assistance of the Caire ICH system. Performance was measured with respect to reader agreement, accuracy, sensitivity, and specificity when compared to the ground truth, defined as reader consensus.
resultsCaire ICH improved the inter-reader agreement on average by 5.76% in a dataset with an ICH prevalence of 74.3%. Further, radiologists using Caire ICH detected an average of 18 more ICHs and significantly increased their accuracy by 6.15%, their sensitivity by 4.6%, and their specificity by 10.62%. The Caire ICH system also improved the radiologist's ability to accurately identify the ICH subtypes present.
conclusionThe Caire ICH device significantly improves the performance of a cohort of radiologists. Such a device has the potential to be a tool that can improve patient outcomes and reduce misdiagnosis of ICH.
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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.