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PT  - JOURNAL ARTICLE
AU  - Walston, Shannon L.
AU  - Tatekawa, Hiroyuki
AU  - Takita, Hirotaka
AU  - Miki, Yukio
AU  - Ueda, Daiju
TI  - Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review
AID  - 10.3174/ajnr.A8211
DP  - 2024 Jun 01
TA  - American Journal of Neuroradiology
PG  - 826--832
VI  - 45
IP  - 6
4099  - http://www.ajnr.org/content/45/6/826.short
4100  - http://www.ajnr.org/content/45/6/826.full
SO  - Am. J. Neuroradiol.2024 Jun 01; 45
AB  - BACKGROUND: Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another.PURPOSE: This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging.DATA SOURCES: PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence–based image translation models of radiologic brain images.STUDY SELECTION: This review collected 102 works published between April 2017 and August 2023.DATA ANALYSIS: Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test.DATA SYNTHESIS: Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P &amp;lt; .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions.LIMITATIONS: Our review is limited by the study design and model heterogeneity.CONCLUSIONS: Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.AIartificial intelligence