Trial reportPloS one2021
Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.
Trial report in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- The ethical and regulatory framework of medical artificial intelligence (AI) system's behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope.PLOS digital health · 2026Article
- Performance of an adult-trained AI tool for intracranial hemorrhage detection on head CT in children aged 6-17 years.Pediatric radiology · 2026Article
- Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography.The Cochrane database of systematic reviews · 2026Article
- Real-world performance evaluation of a commercial deep learning model for intracranial hemorrhage detection.NPJ digital medicine · 2025Article
- Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.AJNR. American journal of neuroradiology · 2025Article
- Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine.World journal of critical care medicine · 2025Review
- 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
- Evaluation of Advanced Artificial Intelligence Algorithms' Diagnostic Efficacy in Acute Ischemic Stroke: A Comparative Analysis of ChatGPT-4o and Claude 3.5 Sonnet Models.Journal of clinical medicine · 2025Article
- Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis.Brain & spine · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.Emergency radiology · 2024Review
- Article
- Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.Radiology. Artificial intelligence · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Clinical Impact of an AI Decision Support System for Detection of Intracranial Hemorrhage in CT Scans.Neurotrauma reports · 2024Article
- Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHighly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services.
methodsIn a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors.
results4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results.
conclusionComplementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT.
trial registrationGerman Clinical Trials Register (DRKS-ID: DRKS00023593).
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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.