Abstract WMP23: Automated ASPECTS Scoring of CT Scans for Acute Ischemic Stroke Patients Using Machine Learning
| Autor: | Sung I Sohn, Michael D. Hill, Alexis T Wilson, Andrew M. Demchuk, Mayank Goyal, Bijoy K Menon, Ericka Teleg, Hulin Kuang, Mohamed Najm, Wu Qiu |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Stroke. 49 |
| ISSN: | 1524-4628 0039-2499 |
| DOI: | 10.1161/str.49.suppl_1.wmp23 |
| Popis: | Objective: The Alberta Stroke Program Early CT Score (ASPECTS) method has been widely used to assess non-contrast CT scans from acute ischemic stroke (AIS) patients. Although the ASPECTS is a simple and systematic approach, ASPECTS scoring accuracy and reliability is still a challenge to clinicians, especially with limited experience. The objective of this study is to develop an automated ASPECTS scoring method, which could provide objective assessment and decision-making support. Methods: We collected 160 AIS patient NCCT images with thickness of 5mm ( Results: The proposed method generated an individual ASPECTS region level detection accuracy of 85.3% and only a 1-point discrepancy in total ASPECTS scores compared to expert reading on MRI. Bland-Altman plot of automated ASPECTS vs. expert MRI ASPECTS shows good agreement (Figure 1). The automated ASPECTS method has very high agreement (91.3%) and specificity (98.5%) when dichotomized (ASPECTS 0-4 vs. 5-10). Conclusions: The automated ASPECTS scoring approach is reliable and accurate and can potentially be used to make decisions in patients with acute ischemic stroke. |
| Databáze: | OpenAIRE |
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