ArticleFrontiers in radiology2026
Downstream evaluation of synthetic AI-generated T
Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Objectives: The purpose of this study was to evaluate synthetic T Materials and methods: Three DL models were developed for synthesizing T Results: The combined CoNeS + DDPM model performed best in image quality and similarity metrics on the test set. Bootstrap analysis revealed that radiomics-based ML and deep learning-based classifiers exhibited distinct characteristics, each of which outperforming the other in different metrics. Conclusions: The present study demonstrates significant advances in medical image AI synthesis by integrating stable diffusion and conditional neural fields, improving the overall quality of synthetic T
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