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Real-world Independent Testing of e-ASPECTS Software (RITeS): statistical analysis plan [version 1; peer review: 2 approved]
- Source :
- AMRC Open Research, Vol 2 (2020)
- Publication Year :
- 2020
- Publisher :
- F1000 Research Ltd, 2020.
-
Abstract
- Background: Artificial intelligence-based software may automatically detect ischaemic stroke lesions and provide an Alberta Stroke Program Early CT score (ASPECTS) on CT, and identify arterial occlusion and provide a collateral score on CTA. Large-scale independent testing will inform clinical use, but is lacking. We aim to test e-ASPECTS and e-CTA (Brainomix, Oxford UK) using CT scans obtained from a range of clinical studies. Methods: Using prospectively collected baseline CT and CTA scans from 10 national/international clinical stroke trials or registries (total >6600 patients), we will select a large clinically representative sample for testing e-ASPECTS and e-CTA compared to previously acquired independent expert human interpretation (reference standard). Our primary aims are to test agreement between software-derived and masked human expert ASPECTS, and the diagnostic accuracy of e-ASPECTS for identifying all causes of stroke symptoms using follow-up imaging and final clinical opinion as diagnostic ground truth. Our secondary aims are to test when and why e-ASPECTS is more or less accurate, or succeeds/fails to produce results, agreement between e-CTA and human expert CTA interpretation, and repeatability of e-ASPECTS/e-CTA results. All testing will be conducted on an intention-to-analyse basis. We will assess agreement between software and expert-human ratings and test the diagnostic accuracy of software. Conclusions: RITeS will provide comprehensive, robust and representative testing of e-ASPECTS and e-CTA against the current gold-standard, expert-human interpretation.
- Subjects :
- stroke
CT
machine learning
automated assessment
eng
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 25176900
- Volume :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- AMRC Open Research
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1b6aa2002bf44962b52b3337b2a03f5b
- Document Type :
- article
- Full Text :
- https://doi.org/10.12688/amrcopenres.12904.1