ArticleJapanese journal of radiology2020
Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance.
Article in Japanese journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Acute ischemic stroke patient factors associated with poor outcomes in patients with favorable collaterals and successful thrombectomy.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2025Article
- 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
- Artificial Intelligence in Stroke Imaging: A Comprehensive Review.The Eurasian journal of medicine · 2023Article
- Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges.The EPMA journal · 2023Review
- Vessel wall MR imaging in neuroradiology.La Radiologia medica · 2022Review
- Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.BioMed research international · 2022Review
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists. MATERIALS AND
methodsWe obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample.
resultsThe diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test.
conclusionThe ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.
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