Fig. 7.

Integration of machine learning in bone/cartilage organoid development and evolution. a Machine Learning-Driven Optimization of Bone/Cartilage Organoids: This panel illustrates the use of machine learning to enhance organoid development. Key variables, including cell selection, matrix gel, assembly techniques, and biological activity, are forecasted and fed into a machine-learning model. The model decodes these inputs into outputs that optimize organoid structural and biological features. Data acquisition, such as image data and omics analysis, is processed to fine-tune the organoid development process through feedback optimization and in vivo validation. The algorithm continuously learns from this feedback loop, improving the design and function of organoids for regenerative purposes. b Evolution of Bone Organoid Modeling: This panel shows the progression of bone/cartilage organoid development through four stages: 1.0-Mimicking physiological characteristics of bone. 2.0-Mimicking pathological characteristics for disease modeling. 3.0-Mimicking structural characteristics, including complex features like osteons. 4.0-Advancing towards clinical translation, where bone/cartilage organoids can be applied in therapeutic contexts, including personalized medicine and large-scale tissue engineering