Cardiac aging synthesis from cross-sectional data with conditional generative adversarial networks

Abstract

Age has important implications for health, and understanding how age manifests in the human body is the first step for a potential intervention. This becomes especially important for cardiac health, since age is the main risk factor for development of cardiovascular disease. Data-driven modeling of age progression has been conducted successfully in diverse applications such as face or brain aging. While longitudinal data is the preferred option for training deep learning models, collecting such a dataset is usually very costly, especially in medical imaging. In this work, a conditional generative adversarial network is proposed to synthesize older and younger versions of a heart scan by using only cross-sectional data. We train our model with more than 14,000 different scans from the UK Biobank. The induced modifications focused mainly on the interventricular septum and the aorta, which is consistent with the existing literature in cardiac aging. We evaluate the results by measuring image quality, the mean absolute error for predicted age using a pre-trained regressor, and demonstrate the application of synthetic data for counter-balancing biased datasets. The results suggest that the proposed approach is able to model realistic changes in the heart using only cross-sectional data and that these data can be used to correct age bias in a dataset.

Adversarial counterfactual augmentation: application in Alzheimer’s disease classification

“…The synthesis model used in this work uses two generators for ageing and rejuvenation. Others have shown that one model can handle both tasks albeit in another dataset and with less conditioning factors ( 55 ). We do highlight though that our approach is agnostic to the generator used and since could benefit from advances in (conditional) generative modelling.…”

“…The proposed adversarial counterfactual scheme could be applied to generative models that produced other types of counterfactuals rather than the ageing brain, e.g. the ageing heart ( 55 , 56 ), future disease outcomes ( 57 ), existence of pathology ( 58 , 59 ), etc. The way we updated the conditional factor (target age) could be improved.…”

Addressing 6 challenges in generative AI for digital health: A scoping review

“…In genetics and pharmaceutical research, generative AI can analyze the chemical structures of existing drugs (using, e.g., SMILES [ 64 ], and generate new molecular structures that are likely to have desired therapeutic effects. Generative adversarial networks can also generate synthetic data, helpful for protecting patient privacy and harnessing the generative capabilities [ 65 ]. Despite the focus on OpenAI’s models, our review also uncovered other models in development that may be of interest to digital health practitioners [ 66 , 67 ].…”

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

“…Reynaud et al [ 40 ] proposed a causal generative model to generate synthetic 3D ultrasound videos conditioned on a given input image and an expected ejection fraction. Campello et al [ 9 ] proposed a conditional generative model in cardiac imaging to extract longitudinal patterns related to aging. Duchateau et al [ 17 ] built a scheme for synthesizing pathological cardiac sequences from real healthy sequences.…”