ArticleJournal of medical imaging (Bellingham, Wash.)2015
Automated brain computed tomographic densitometry of early ischemic changes in acute stroke.
Article in Journal of medical imaging (Bellingham, Wash.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 28 citations in OpenAlex.
- Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025Article
- Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.Journal of imaging informatics in medicine · 2025Article
- Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.Frontiers in neurology · 2024Article
- Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke.Frontiers in neurology · 2023Article
- The effects of the skull on CT imaging of the brain: a skull and brain phantom study.The British journal of radiology · 2021Article
- Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study.Frontiers in pediatrics · 2021Article
- Article
- Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.AJNR. American journal of neuroradiology · 2019Article
- Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software.Neuroradiology · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS ([Formula: see text] and [Formula: see text], respectively) and with follow-up mRS ([Formula: see text] and [Formula: see text]), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences [Formula: see text] point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.
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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.