Bioprinting is an emerging additive manufacturing technology that has enormous potential in bone implantation and repair. The insufficient accuracy of the shape of bioprinted parts is a primary clinical barrier that prevents widespread utilization of bioprinting, especially for bone design with high-resolution requirements. During the last five years, the use of computer vision for process control has been widely practiced in the manufacturing field. Computer vision can improve the performance of bioprinting for bone research with respect to various aspects, including accuracy, resolution, and cell survival rate. Hence, computer vision plays a substantial role in addressing the current defect problem in bioprinting for bone research. In this review, recent advances in the application of computer vision in bioprinting for bone research are summarized and categorized into three groups based on different defect types: bone scaffold process control, deep learning, and cell viability models. The collection of printing parameters, data processing, and feedback of bioprinting information, which ultimately improves printing capabilities, are further discussed. We envision that computer vision may offer opportunities to accelerate bioprinting development and provide a new perception for bone research.

生物打印是一种新兴的增材制造技术，在骨植入和修复方面具有巨大潜力。生物打印部件的形状精度不足是阻碍生物打印广泛应用的主要临床障碍，特别是对于具有高分辨率要求的骨骼设计。在过去五年中，将计算机视觉用于过程控制已在制造领域得到广泛应用。计算机视觉可以提高生物打印在骨骼研究的各个方面的性能，包括准确性、分辨率和细胞存活率。因此，计算机视觉在解决目前用于骨骼研究的生物打印缺陷问题方面发挥着重要作用。在本次审查中，总结了计算机视觉在骨骼研究生物打印中应用的最新进展，并根据不同的缺陷类型分为三组：骨支架过程控制、深度学习和细胞活力模型。进一步讨论了打印参数的收集、数据处理和生物打印信息的反馈，最终提高了打印能力。我们设想计算机视觉可能会提供加速生物打印发展的机会，并为骨骼研究提供新的认识。进一步讨论。我们设想计算机视觉可能会提供加速生物打印发展的机会，并为骨骼研究提供新的认识。进一步讨论。我们设想计算机视觉可能会提供加速生物打印发展的机会，并为骨骼研究提供新的认识。