ArticleDiagnostics (Basel, Switzerland)2022
Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography.
Article in Diagnostics (Basel, Switzerland), 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 8 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
8 citing papers in PubMed, 13 citations in OpenAlex.
- Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.Radiology · 2024Trial
- AI assistance improves radiology resident reader performance in CT diagnosis of intracranial hemorrhage.La Radiologia medica · 2026Article
- Implementation of AI in radiology: the perspective of referring physicians.Insights into imaging · 2025Article
- Revolution or risk?-Assessing the potential and challenges of GPT-4V in radiologic image interpretation.European radiology · 2025Article
- Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.Healthcare (Basel, Switzerland) · 2024Article
- The virtual reference radiologist: comprehensive AI assistance for clinical image reading and interpretation.European radiology · 2024Article
- AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.BMJ open · 2024Article
- AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.Insights into imaging · 2023Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies.
methodsFour neuroradiologists with 1-10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs.
resultsAI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans (
conclusionOur study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention.
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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.