The musculoskeletal (MS) system consists of bone, cartilage, tendon, ligament, and skeletal muscle, which forms the basic framework of the human body. This system plays a vital role in appropriate body functions, including movement, the protection of internal organs, support, hematopoiesis, and postural stability. Therefore, it is understandable that the damage or loss of MS tissues significantly reduces the quality of life and limits mobility. Tissue engineering and its applications in the healthcare industry have been rapidly growing over the past few decades. Tissue engineering has made significant contributions toward developing new therapeutic strategies for the treatment of MS defects and relevant disease. Among various biomaterials used for tissue engineering, natural polymers offer superior properties that promote optimal cell interaction and desired biological function. Natural polymers have similarity with the native ECM, including enzymatic degradation, bio-resorb and non-toxic degradation products, ability to conjugate with various agents, and high chemical versatility, biocompatibility, and bioactivity that promote optimal cell interaction and desired biological functions. This review summarizes recent advances in applying natural-based scaffolds for musculoskeletal tissue engineering.

3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and replicating complex patterns that surpass human capabilities. However, the integration of AI in tissue engineering is often hampered by the lack of comprehensive and reliable data. This study addresses these challenges by providing one of the most extensive datasets on 3D-printed scaffolds. It provides the most comprehensive open-source dataset and employs various AI techniques, from unsupervised to supervised learning. This dataset includes detailed information on 1171 scaffolds, featuring a variety of biomaterials and concentrations—including 60 biomaterials such as natural and synthesized biomaterials, crosslinkers, enzymes, etc.—along with 49 cell lines, cell densities, and different printing conditions. We used over 40 machine learning and deep learning algorithms, tuning their hyperparameters to reveal hidden patterns and predict cell response, printability, and scaffold quality. The clustering analysis using KMeans identified five distinct ones. In classification tasks, algorithms such as XGBoost, Gradient Boosting, Extra Trees Classifier, Random Forest Classifier, and LightGBM demonstrated superior performance, achieving higher accuracy and F1 scores. A fully connected neural network with six hidden layers from scratch was developed, precisely tuning its hyperparameters for accurate predictions. The developed dataset and the associated code are publicly available on https://github.com/saeedrafieyan/MLATE to promote future research.

3D (bio)printing is increasingly used for scaffold fabrication in tissue engineering; however, identifying suitable biomaterial compositions, cell types, cell densities, and printing conditions remains largely empirical, time-consuming, and costly. Practical application of machine learning (ML) in this field is further limited by the scarcity of comprehensive datasets based on pre-fabrication variables, the lack of integrated frameworks for prioritizing candidate scaffold formulations, and the limited availability of accessible tools for non-technical users. To address these challenges, we developed MLATE V3 (Machine Learning Applications in Tissue Engineering V3), an open-source, data-driven, multi-tissue framework for scaffold prediction, prioritization, and experimental planning. An open-source dataset comprising 2646 samples, 130 biomaterials, and 186 cell lines, including co-culture systems, across 18 tissue engineering contexts was compiled and used to benchmark ML, deep learning, tabular transformer-based, and tabular foundation models for predicting cell response and printability from pre-fabrication variables. The best-performing models achieved weighted F 1-scores of 0.773 for cell response and 0.806 for printability. These predictive outputs were subsequently integrated through the Weighted Synergistic Scaffold Quality (WSSQ) metric, which combines cell response and printability using adjustable weights to provide a unified criterion for candidate ranking. WSSQ was then incorporated into a Bayesian optimization framework to search user-defined biomaterial compositions and printing-parameter ranges and prioritize candidate scaffold formulations for experimental evaluation. To make the complete workflow accessible to researchers, MLATE V3 was implemented as an open-source web platform that enables users to select predictive models, define design spaces, perform WSSQ-based optimization, and generate step-by-step fabrication protocols through an LLM-assisted procedure-generation module. Overall, MLATE V3 provides an extensible decision-support framework that connects literature-derived data, predictive modeling, multi-objective scaffold ranking, optimization, and experimental planning. The platform is intended to narrow the experimental search space and generate candidate formulations for subsequent experimental validation rather than to serve as an experimentally validated optimizer.