Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)
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...
Gespeichert in:
Veröffentlicht in: | Neuroradiology 2023-05, Vol.65 (5), p.907-913 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
ISSN: | 0028-3940 1432-1920 1432-1920 |
DOI: | 10.1007/s00234-023-03126-9 |