Variational Autoencoders for Data Augmentation in Clinical Studies

Abstract

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials.

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

“…Utilizing clinical data generated by VAE enhanced the statistical power of the studies. The incidence of type I error remained low and consistent with the levels observed in the actual dataset, whereas the statistical power of the VAE approach was even higher compared to that observed in the original datasets [10].…”

“…Recently, our research group introduced the idea of using an artificial neural network, specifically variational autoencoders (VAEs), in clinical studies [10]. VAE models, falling within the category of generative models, leverage deep learning to generate new data once trained.…”

“…VAE models utilize two functions: the encoder, responsible for encoding input data, and the decoder, tasked with rebuilding the input data using the output generated by the encoder. This deep learning method was found to offer several advantages in clinical trials with a parallel design [10]. Utilizing clinical data generated by VAE enhanced the statistical power of the studies.…”

Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies

“…To our knowledge, there has been no prior exploration of implementing the WGAN algorithm to generate "virtual patients" aimed at reducing the time and expenses associated with clinical studies and minimizing human exposure. Two recent studies [17,18] by our research group, highlighted the positive role of variational autoencoders in clinical and bioequivalence trials. This study expands these two previous works by using an alternative generative AI algorithm, the Wasserstein Generative Adversarial Networks.…”

“…In healthcare, there are numerous use cases of AI and machine learning, including drug discovery, medicine, dentistry, anesthesiology, and ophthalmology [11][12][13][14][15][16]. One recent application of AI proposed by our research group is data augmentation, which involves virtually increasing a sample by generating new data from existing data [17,18]. In this context, several other studies have evaluated the effectiveness of diverse augmentation approaches where the Wasserstein Generative Adversarial Networks (WGANs) have exhibited superior performance compared with other methods [19].…”