The anterior cruciate ligament (ACL) is one of the most injured ligaments, with approximately 100,000 ACL reconstructions taking place annually in the United States. In order to successfully manage ACL rupture, it is of the utmost importance to understand the anatomy, unique physiology, and biomechanics of the ACL, as well as the injury mechanisms and healing capacity. Currently, the “gold standard” for the treatment of ACL ruptures is surgical reconstruction, particularly for young patients or athletes expecting to return to pivoting sports. Although ACL reconstruction boasts a high success rate, patients may face different, serious post-operative complications, depending on the type of graft and technique used in each one of them. Tissue engineering is a multidisciplinary field that could contribute to the formation of a tissue-engineered ACL graft manufactured by a combination of the appropriate stem-cell type, a suitable scaffold, and specific growth factors, combined with mechanical stimuli. In this review, we discuss the aspects that constitute the creation of a successful tissue-engineered graft while also underlining the current drawbacks that arise for each issue. Finally, we highlight the benefits of incorporating new technologies like artificial intelligence and machine learning that could revolutionize tissue engineering.

Background: Total joint arthroplasties (TJAs) are well-established orthopedic procedures that provide effective pain relief and restore functional mobility. Although these procedures are generally highly successful, postoperative complications may still occur. This review examined the literature on how machine learning (ML) can predict postoperative complications based on laboratory and clinical parameters for total knee (TKA) and total hip arthroplasty (THA). Methods: A scoping review was conducted and reported in accordance with the PRISMA criteria for scoping reviews (PRISMA-ScR). We searched PubMed/MEDLINE, Scopus, and EMBASE for studies evaluating the use of ML to predict adverse events following TKA and/or THA, using routinely available laboratory values combined with demographics and clinical parameters. Results: The initial search yielded 144 studies, of which 18 were finally deemed eligible for inclusion. Key predictors of complications in the examined studies included hemoglobin, hematocrit, white blood cell count, platelet count, and renal markers. Need for transfusion was the most frequently studied outcome, followed by severe adverse events and postoperative delirium. The ML models, especially gradient boosting machines (GBMs), random forests (RFs), and neural networks (NNs), demonstrated promising discriminative performance in predicting adverse events. Conclusions: Combining laboratory data with demographics and clinical information may improve the accuracy of complication prediction. This review emphasizes the potential of ML analysis of laboratory parameters to create risk assessment tools, which could detect complication risk and potentially improve outcomes for patients undergoing TKA or THA.