Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Cell therapies hold significant promise for the treatment of injured or diseased musculoskeletal tissues. However, despite advances in research, there is growing concern about the increasing number of clinical centres around the world that are making unwarranted claims or are performing risky biological procedures. Such providers have been known to recommend, prescribe, or deliver so called ‘stem cell’ preparations without sufficient data to support their true content and efficacy. In this annotation, we outline the current environment of stem cell-based treatments and the strategies of marketing directly to consumers. We also outline the difficulties in the regulation of these clinics and make recommendations for best practice and the identification and reporting of illegitimate providers. Cite this article:
There is good scientific rationale to support the use of growth factors to promote musculoskeletal tissue regeneration. However, the clinical effectiveness of platelet-rich plasma (PRP) and other blood-derived products has yet to be proven. Characterization and reporting of PRP preparation protocols utilized in clinical trials for the treatment of musculoskeletal disease is highly inconsistent, and the majority of studies do not provide sufficient information to allow the protocols to be reproduced. Furthermore, the reporting of blood-derived products in orthopaedics is limited by the multiple PRP classification systems available, which makes comparison of results between studies challenging. Several attempts have been made to characterize and classify PRP; however, no consensus has been reached, and there is lack of a comprehensive and validated classification. In this annotation, we outline existing systems used to classify preparations of PRP, highlighting their advantages and limitations. There remains a need for standardized universal nomenclature to describe biological therapies, as well as a comprehensive and reproducible classification system for autologous blood-derived products. Cite this article:
