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. 2023 May;65(5):907-913.
doi: 10.1007/s00234-023-03126-9. Epub 2023 Feb 7.

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

Abhishta Bhandari  1   2 , Luke Scott  3 , Manuela Weilbach  4 , Ravi Marwah  5   6 , Arian Lasocki  7   8
Affiliations

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

Abhishta Bhandari et al. Neuroradiology. 2023 May.

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.

Keywords: Artificial intelligence; Deep learning; Glioma; Machine learning; Quality.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Number of articles by year, type of journal, AI task and 3 most frequently utilised algorithms (abbreviations - TTP true tumour progression, TRE treatment-related effects, CNN convolutional neural network, RF random forest, SVM support vector machine)
Fig. 2
Fig. 2
CLAIM scores by year, AI task, journal type and 3 most frequently utilised AI algorithms (abbreviations - TTP true tumour progression, TRE treatment-related effects, CNN convolutional neural network, RF random forest, SVM support vector machine)
Fig. 3
Fig. 3
Percentage of studies fulfilling CLAIM criteria by item number
See this image and copyright information in PMC

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