Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)

Bhandari, A; Scott, L; Weilbach, M; Marwah, R; Lasocki, A

Author(s): Bhandari, A; Scott, L; Weilbach, M; Marwah, R; Lasocki, A;
Details: Publication Year 2023-05,Volume 65,Issue #5,Page 907-913
Journal Title: Neuroradiology
Publication Type: Research article
Abstract: PURPOSE: 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. METHODS: A literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021 RESULTS: A total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10-31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance. CONCLUSION: AI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration.
Publisher: Springer Nature
Keywords: Humans; *Artificial Intelligence; Checklist; Radiography; Magnetic Resonance Imaging; *Glioma/diagnostic imaging; Artificial intelligence; Deep learning; Glioma; Machine learning; Quality
Department(s): Cancer Imaging
PubMed ID: 36746792
Publisher's Version: https://doi.org/10.1007/s00234-023-03126-9
Open Access at Publisher's Site: https://doi.org/10.1007/s00234-023-03126-9
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2023-08-08 01:28:49

Last Modified: 2023-08-08 01:31:07