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resolution, freeform lattice and patterned structures accelerating experimental iterations, thereby significantly
compared to other 3D printing methods. David Collins’ improving design efficiency and functionality. Future research
team developed a 3D printing technique called “Dynamic will further explore the synergistic application of deep
Interface Printing”, which rapidly generates centimeter- computing and AI-assisted design with other engineering
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scale, single-cell-resolution 3D structures in seconds. This strategies, such as omics analysis and 3D printing, to provide
method successfully printed objects measuring 3 cm in comprehensive support for tendon organoid construction.
diameter and 7 cm in length, with a resolution of 15 µm.
The progress in 3D printing technology facilitates the 5. Applications
simulation of complex tissue architectures and optimizes Although still in the early stages of development, tendon
the spatial configuration of tendon organoid construction. organoids offer significant advantages over traditional 2D
tissue cultures and animal models, as they more accurately
4.4.3. Prediction, evaluation, and optimized design for replicate key aspects of tendon biology in vitro. These
data analysis based on AI
organoids provide structured and reproducible cellular
The application of data analysis technologies provides constructs that mimic the 3D architecture and cellular
powerful tools for optimizing design. By integrating multi- composition of human tendons. As Figure 3 demonstrates,
source data, including omics, mechanical properties, and tendon organoids are valuable tools for applications in
imaging data, and combining advanced algorithms and regenerative medicine, disease modeling, drug screening,
models, researchers can extract critical information from and biomechanics.
vast datasets to optimize the design parameters of tendon
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organoids, thereby enhancing their biomimicry and 5.1. Regenerative medicine
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functionality. Tendinopathy, rotator cuff tears, and Achilles tendon
Organoid research primarily encompasses three ruptures are common tendon injuries that often result
aspects: Construction strategies, data analysis, and efficacy in chronic pain, restricted mobility, and reduced quality
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verification. In particular, AI and deep learning, with of life. Current treatments, such as autografts and
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their robust capabilities in big data processing, algorithmic allografts, face significant limitations, including donor
computation, and self-learning, can significantly accelerate tissue shortages, immune rejection, and incomplete tendon
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the development of tendon organoid research. 158,159 AI strength restoration.
can process and integrate large volumes of data involved Tendon organoids offer a promising regenerative
in tendon organoid construction, enabling predictive solution by enabling the engineering of functional grafts
modeling and design optimization. Moreover, automation that replicate the biological and biomechanical properties
facilitated by AI also simplifies experiments, improves of damaged tendons. Typically, the process begins
reproducibility, and reduces experimental bias. Trained with patient-derived TSPCs, which are cultured under
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AI algorithms can analyze and extract features from controlled conditions to differentiate into tendon-like cells,
extensive imaging data, 160,161 reconstruct 3D structures or tenocytes. Compared to direct cell therapy, organoids
of tendon organoids from 2D imaging data, and monitor more closely resemble native tendon tissue, demonstrating
dynamic construction processes in real time. In addition, AI higher survival rates and improved integration potential.
can screen ideal small molecule combinations and suitable Integration is further enhanced by vascularization
cell subpopulations from massive omics datasets. 162-164 strategies, such as PDGF-induced angiogenesis, which
By integrating multi-dimensional data, AI can simulate promotes blood vessel formation without excessive
biological processes and use algorithms to analyze and fibrosis. As a result, tendon organoids exhibit prolonged
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predict tendon organoid construction at both microscopic therapeutic effects while requiring fewer transplanted
(cellular) and macroscopic (tissue) levels. Based on these cells. They can also incorporate features like gradient
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predictions, AI can optimize materials, structures, and stiffness to mimic the transition zones between tendons
culture conditions. For example, in a study on AI-assisted and bones or muscles. 171
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3D printing of biomimetic tendon interfaces, researchers
used AI-trained algorithms to define printing parameters. Unlike traditional grafts, they can be patient-specific,
By adjusting exposure time and light intensity during the reducing the risk of immune rejection and enhancing tissue
3D printing process, Kiratitanaporn et al. tailored the integration. Moreover, by leveraging biochemical cues and
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local mechanical properties of the scaffold to match those of growth factors, they facilitate the production of essential
different regions in native tendon tissue. Deep computing ECM components, such as collagen, to support tendon
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and AI-assisted design offer powerful tools for constructing regeneration.
tendon organoids by simulating complex biological and By combining these elements, tendon organoids
mechanical processes, optimizing design parameters, and bridge the gap between biological complexity and clinical

Volume 1 Issue 3 (2025) 13 doi: 10.36922/OR025170016