Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study

Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study

Abstract Early identification of cerebral small vessel disease related cognitive impairment (CSVD-CI) is crucial for timely clinical intervention. We developed a Transformer-based deep learning model using white matter hyperintensity (WMH) radiomics features from T2-fluid-attenuated inversion recove...

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Main Authors: Lili Huang, Zhuoyuan Li, Xiaolei Zhu, Hui Zhao, Chenglu Mao, Zhihong Ke, Yuting Mo, Dan Yang, Yue Cheng, Ruomeng Qin, Zheqi Hu, Pengfei Shao, Ying Chen, Min Lou, Kelei He, Yun Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01813-w
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https://doi.org/10.1038/s41746-025-01813-w

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