Please use this identifier to cite or link to this item:
https://dora.health.qld.gov.au/qldresearchjspui/handle/1/5137| Title: | Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM) | Authors: | Bhandari, Abhishta Scott, Luke Weilbach, Manuela Marwah, Ravi Lasocki, Arian |
Issue Date: | 2023 | Source: | Bhandari, A., Scott, L., Weilbach, M., Marwah, R., & Lasocki, A. (2023). Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM). Neuroradiology, 10.1007/s00234-023-03126-9. Advance online publication. https://doi.org/10.1007/s00234-023-03126-9 | Journal: | Neuroradiology | Abstract: | The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria. | Description: | Cairns & Hinterland Hospital and Health Service (CHHHS) affiliated author: Luke Scott | DOI: | 10.1007/s00234-023-03126-9 | Keywords: | Artificial intelligence;Deep learning;Glioma;Machine learning;Quality | Type: | Article |
| Appears in Sites: | Cairns & Hinterland HHS Publications |
Show full item record
Items in DORA are protected by copyright, with all rights reserved, unless otherwise indicated.