| Title: | AI-driven preclinical disease risk assessment using imaging in UK biobank |
| Journal: | npj Digital Medicine |
| Published: | 26 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40715621/ |
| DOI: | https://doi.org/10.1038/s41746-025-01771-3 |
| URL: | https://www.nature.com/articles/s41746-025-01771-3.pdf |
Publication 16098
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique advantage by capturing detailed structural and functional information. Unlike non-image data, such as lifestyle, sociodemographic, or prior medical conditions, which often rely on self-reported information susceptible to recall biases and subjective perceptions, imaging offers more objective and reliable insights. Although the use of medical imaging in artificial intelligence (AI)-driven risk assessment is growing, its full potential remains underutilized. In this work, we demonstrate how imaging can be integrated into routine screening workflows, in particular by taking advantage of neck-to-knee whole-body magnetic resonance imaging (MRI) data available in the large prospective study UK Biobank. Our analysis focuses on three-year risk assessment for a broad spectrum of diseases, including cardiovascular, digestive, metabolic, inflammatory, degenerative, and oncologic conditions. We evaluate AI-based pipelines for processing whole-body MRI and demonstrate that using image-derived radiomics features provides the best prediction performance, interpretability, and integration capability with non-image data.</p>
7 Authors
- Dmitrii Seletkov
- Sophie Starck
- Tamara T. Mueller
- Yundi Zhang
- Lisa Steinhelfer
- Daniel Rueckert
- Rickmer Braren
1 Application
| Application ID | Title |
|---|---|
| 87802 | Assessment of frailty and biological age using multi-modal deep learning |
Enabling scientific discoveries that improve human health