| Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images A Le Goallec, S Diai, S Collin, JB Prost, T Vincent, CJ Patel Nature Communications 13 (1), 1979, 2022 | 30 | 2022 |
| Dissecting heart age using cardiac magnetic resonance videos, electrocardiograms, biobanks, and deep learning AL Goallec, JB Prost, S Collin, S Diai, T Vincent, CJ Patel medRxiv, 2021.06. 09.21258645, 2021 | 14 | 2021 |
| Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictors A Le Goallec, S Collin, S Diai, JB Prost, MH Jabri, T Vincent, CJ Patel medRxiv, 2021.04. 25.21255767, 2021 | 13 | 2021 |
| Predicting arterial age using carotid ultrasound images, pulse wave analysis records, cardiovascular biomarkers and deep learning A Le Goallec, S Collin, S Diai, T Vincent, CJ Patel medRxiv, 2021.06. 17.21259120, 2021 | 5 | 2021 |
| Using deep learning to analyze the compositeness of musculoskeletal aging reveals that spine, hip and knee age at different rates, and are associated with different genetic and … AL Goallec, S Diai, S Collin, T Vincent, CJ Patel medRxiv, 2021.06. 14.21258896, 2021 | 4 | 2021 |
| Identifying the genetic and non-genetic factors associated with accelerated eye aging by using deep learning to predict age from fundus and optical coherence tomography images A Le Goallec, S Diai, S Collin, T Vincent, CJ Patel medRxiv, 2021 | 4 | 2021 |
| Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale A Le Goallec, S Collin, MH Jabri, S Diai, T Vincent, CJ Patel PLOS Digital Health 2 (1), e0000176, 2023 | 3 | 2023 |
| Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and … AL Goallec, S Diai, S Collin, T Vincent, CJ Patel medRxiv, 2021.06. 22.21259280, 2021 | 3 | 2021 |
| Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging A Le Goallec, S Diai, S Collin, T Vincent, CJ Patel medRxiv, 2021.06. 24.21259471, 2021 | 1 | 2021 |
| Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging A Le Goallec, S Diai, S Collin, JB Prost, T Vincent, CJ Patel medRxiv, 2021.06. 24.21259492, 2021 | 1 | 2021 |
| Comparing the genetic and environmental architecture of blood count, blood biochemistry and urine biochemistry biological ages with machine learning A Le Goallec, S Diai, T Vincent, CJ Patel medRxiv, 2021.07. 05.21260032, 2021 | | 2021 |
| Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging AL Goallec, S Diai, T Vincent, CJ Patel medRxiv, 2021.07. 05.21260048, 2021 | | 2021 |
| Predicting age from 100,000 one week-long 100Hz wrist accelerometer records of physical activity A Le Goallec, S Collin, MH Jabri, S Diai, T Vincent, CJ Patel medRxiv, 2021.06. 21.21259265, 2021 | | 2021 |
Chirag J PatelAssociate Professor of Biomedical Informatics, Harvard University在 hms.harvard.edu 的电子邮件经过验证
