ArticleProceedings of the National Academy of Sciences of the United States of America2024
Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.Nature medicine · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- Accelerated Biological Aging Increases the Risk of Head and Neck Cancer: Insights From Genetic Instruments of Epigenetic Clocks.Molecular carcinogenesis · 2026Article
- Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.Nature communications · 2026Article
- Age-adjusted leukocyte telomere length predicts long-term mortality in older patients discharged from acute care hospitals.GeroScience · 2026Article
- Data and knowledge-driven imaging biomarkers for lumbar aging and degenerative risk stratification monitoring.NPJ digital medicine · 2026Article
- A new framework for facial age estimation in humans and AIs.Scientific reports · 2026Article
- Face aging rate quantifies change in biological age to predict cancer outcomes.Nature communications · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Improving Face Age Prediction by Using Multiple-Angle Photos.Computational and structural biotechnology journal · 2026Article
- Synergistic and heterogeneous aging using composite phenotypes and multiple organ systems aging clocks.Communications medicine · 2025Article
- A full life cycle biological clock based on routine clinical data and its impact in health and diseases.Nature medicine · 2025Article
- AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration.Cell reports. Medicine · 2025Article
- Artificial intelligence-derived retinal age gap as a marker for reproductive aging in women.NPJ digital medicine · 2025Article
- DNA methylation and prediction of biological age.Frontiers in molecular biosciences · 2025Review
- Association between gamma-glutamyl transferase levels and the retinal age gap.Frontiers in physiology · 2025Article
- Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.Brain sciences · 2024Article
- Review
- Concepts and applications of digital twins in healthcare and medicine.Patterns (New York, N.Y.) · 2024Review
- Review
Corrections and comments
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Authors and funding
19 authors at 8 institutions in 2 countries.
Funding
Abstract
Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.