Deep learning for polymer scaffold bioimage analysis: Opportunities and challenges

Significant efforts have been made to advance bioprinted scaffold research in cell biology, tissue engineering, and drug screening studies. Ideal scaffolds should demonstrate suitable mechanical properties, excellent biocompatibility, and bioactivities. However, the design and preparation of such scaffolds are challenging. Imaging modalities, including magnetic resonance imaging, micro-computed tomography, ultrasound imaging, optical coherence tomography, and confocal laser scanning microscopy, are commonly used to visualize the interior architecture of bioprinted scaffolds, as well as the surrounding cells and tissues. The obtained bioimages provide direct insight into the biological functionalities of the scaffold, though their interpretation may lead to differing viewpoints and even debates. This review explores deep learning (DL) methods employed for image analysis, including restoration, segmentation, and classification. First, current DL methods for biological image processing are summarized, such as convolutional neural network, U-Net, and generative adversarial network. The corresponding outcomes of these methods reveal cell–scaffold and tissue–scaffold interactions, providing guidance for scaffold design in specific applications. Thereafter, the challenges and limitations of DL applications are highlighted, such as building DL models using smaller bioimage datasets, interpreting DL models, vision-language model-guided bioimage analysis, and developing intelligent analysis platforms. Hence, this review would mark a paradigm shift in polymer scaffold designs and the associated performance.