Deep learning in rib fracture imaging: study quality assessment using the Must AI Criteria-10 (MAIC-10) checklist for artificial intelligence in medical imaging

Abstract

OBJECTIVES: To analyze the methodological quality of studies on deep learning (DL) in rib fracture imaging with the Must AI Criteria-10 (MAIC-10) checklist, and to report insights and experiences regarding the applicability of the MAIC-10 checklist. MATERIALS AND METHODS: An electronic literature search was conducted on the PubMed database. After selection of articles, three radiologists independently rated the articles according to MAIC-10. Differences of the MAIC-10 score for each checklist item were assessed using the Fleiss' kappa coefficient. RESULTS: A total of 25 original articles discussing DL applications in rib fracture imaging were identified. Most studies focused on fracture detection (n = 21, 84%). In most of the research papers, internal cross-validation of the dataset was performed (n = 16, 64%), while only six studies (24%) conducted external validation. The mean MAIC-10 score of the 25 studies was 5.63 (SD, 1.84; range 1-8), with the item "clinical need" being reported most consistently (100%) and the item "study design" being most frequently reported incompletely (94.8%). The average inter-rater agreement for the MAIC-10 score was 0.771. CONCLUSIONS: The MAIC-10 checklist is a valid tool for assessing the quality of AI research in medical imaging with good inter-rater agreement. With regard to rib fracture imaging, items such as "study design", "explainability", and "transparency" were often not comprehensively addressed. CRITICAL RELEVANCE STATEMENT: AI in medical imaging has become increasingly common. Therefore, quality control systems of published literature such as the MAIC-10 checklist are needed to ensure high quality research output. KEY POINTS: Quality control systems are needed for research on AI in medical imaging. The MAIC-10 checklist is a valid tool to assess AI in medical imaging research quality. Checklist items such as "study design", "explainability", and "transparency" are frequently addressed incomprehensively.

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