Engineered tissue constructs are typically evaluated by extensive animal studies to assess their in vivo efficacy. Histological analysis is the widely used approach. However, this technique is time-consuming and highly dependent on experienced pathologists. Moreover, it provides limited insight into how tissue continuously changes at the same tissue site over time. In this study, we propose a deep learning (DL)-based model, Bone Tissue Prediction Generative Adversarial Network (BTP-GAN), which integrates synthetic histological image generation with a biological tissue growth model to simulate bone tissue development over time. Despite a small training dataset, the generated histological images are biologically meaningful and realistic, owing to three algorithmic modules: the Analysis module, Growth module, and Generation module. Moreover, Ordinary Differential Equations (ODE)-Transformer model can learn the temporal patterns and structural characteristics of real tissue images, enabling the generation of histological images according to various specific generative conditions. The BTP-GAN successfully demonstrates the continuous histological change at the same bone tissue site, relying only on a single input histological image. This in-silico animal study may change the perception of the necessity of extensive animal studies in tissue engineering and provide a practical framework for the future advancement of in-silico tissue engineering.
Yao et al. (Mon,) studied this question.