ArticleHuman brain mapping2022
Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 44 citations in OpenAlex.
- A Location-Specific Hyperdense Area Score Predicts Severe Mass Effect after Thrombectomy.Neurocritical care · 2026Article
- Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.BMC medicine · 2026Article
- Role of high-density area score in predicting outcomes of large-vessel occlusion stroke after endovascular treatment.European radiology · 2026Article
- Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.BMC medical imaging · 2026Article
- Evaluation of ChatGPT-5 for Automated ASPECTS Assessment on Non-Contrast CT in Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Brain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans.Journal of imaging informatics in medicine · 2025Article
- Core-Penumbra Hyperacute Ischemic Stroke Dataset.Scientific data · 2025Article
- Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.Journal of imaging informatics in medicine · 2025Article
- Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm.Stroke (Hoboken, N.J.) · 2025Article
- Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.European radiology · 2025Article
- Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.European radiology experimental · 2025Article
- Prediction of prognosis in acute ischemic stroke after mechanical thrombectomy based on multimodal MRI radiomics and deep learning.Frontiers in neurology · 2025Article
- Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort.European radiology · 2024Article
- Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.Frontiers in neurology · 2024Article
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review.Frontiers in stroke · 2023Review
- Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.Human brain mapping · 2022Article
- Extracellular traps and the role in thrombosis.Frontiers in cardiovascular medicine · 2022Review
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Authors and funding
13 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.
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