Machine-learning for osteoarthritis research

Elucidating Causal Relationships Among Gut Microbiota, Human Blood Metabolites, and Knee Osteoarthritis: Evidence from a Two-Stage Mendelian Randomization Analysis

Background: Although previous observational studies suggest a potential association between gut microbiota (GM) and knee osteoarthritis (KOA), the causal relationships remain unclear, particularly concerning the role of blood metabolites (BMs) as potential mediators. Elucidating these interactions is crucial for understanding the mechanisms underlying KOA progression and may inform the development of novel therapeutic strategies. Objective: This study aimed to determine the causal relationship between GM and KOA and to quantify the potential mediating role of BMs. Methods: Instrumental variables (IVs) for GM and BMs were retrieved from the MiBioGen consortium and metabolomics genome-wide association studies (GWAS) databases. KOA-associated single-nucleotide polymorphisms were sourced from the FinnGen consortium. Inverse-variance weighted approach was utilized as the main analytical method for Mendelian randomization (MR) analysis, complemented by MR-Egger, simple mode, weighted mode, and weighted median methods. The causal relationships between GM, BMs, and KOA were sequentially analyzed by multivariate MR. False discovery rate correction was applied to account for multiple comparisons in the MR results. Sensitivity analyses and reverse MR analysis were also conducted to verify the reliability of the findings. Finally, a two-step approach was employed to determine the proportion of BMs mediating the effects of GM on KOA. Results: MR analysis identified seven gut microbial species that are causally associated with KOA. Additionally, MR analysis of 1091 BMs and 309 metabolite ratios revealed 13 metabolites that influence the risk of KOA. Through two-step analysis, three BMs were identified as mediators of the effects of two GMs on KOA. Among them, 6-hydroxyindole sulfate exhibited the highest mediation percentage (10.26%), followed by N-formylanthranilic acid (6.55%). Sensitivity and reverse causality analyses further supported the robustness of these findings. Conclusion: This research identified specific GMs and BMs that have a causal association with KOA. These findings provide critical insights into how GM may influence KOA risk by modulating specific metabolites, which could be valuable for the targeted treatment and prevention of KOA.

A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning

Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review.

A Surgeon's Guide to Understanding Artificial Intelligence and Machine Learning Studies in Orthopaedic Surgery

PURPOSE OF REVIEW

In recent years, machine learning techniques have been increasingly utilized across medicine, impacting the practice and delivery of healthcare. The data-driven nature of orthopaedic surgery presents many targets for improvement through the use of artificial intelligence, which is reflected in the increasing number of publications in the medical literature. However, the unique methodologies utilized in AI studies can present a barrier to its widespread acceptance and use in orthopaedics. The purpose of our review is to provide a tool that can be used by practitioners to better understand and ultimately leverage AI studies.

RECENT FINDINGS

The increasing interest in machine learning across medicine is reflected in a greater utilization of AI in recent medical literature. The process of designing machine learning studies includes study design, model choice, data collection/handling, model development, training, testing, and interpretation. Recent studies leveraging ML in orthopaedics provide useful examples for future research endeavors. This manuscript intends to create a guide discussing the use of machine learning and artificial intelligence in orthopaedic surgery research. Our review outlines the process of creating a machine learning algorithm and discusses the different model types, utilizing examples from recent orthopaedic literature to illustrate the techniques involved.

Mesenchymal Stem Cell-Derived Exosomes and MicroRNAs in Cartilage Regeneration: Biogenesis, Efficacy, miRNA Enrichment and Delivery

Exosomes are the small extracellular vesicles secreted by cells for intercellular communication. Exosomes are rich in therapeutic cargos such as microRNA (miRNA), long non-coding RNA (lncRNA), small interfering RNA (siRNA), DNA, protein, and lipids. Recently, many studies have focused on miRNAs as a promising therapeutic factor to support cartilage regeneration. Exosomes are known to contain a substantial amount of a variety of miRNAs. miRNAs regulate the post-transcriptional gene expression by base-pairing with the target messenger RNA (mRNA), leading to gene silencing. Several exosomal miRNAs have been found to play a role in cartilage regeneration by promoting chondrocyte proliferation and matrix secretion, reducing scar tissue formation, and subsiding inflammation. The exosomal miRNA cargo can be modulated using techniques such as cell transfection and priming as well as post-secretion modifications to upregulate specific miRNAs to enhance the therapeutic effect. Exosomes are delivered to the joints through direct injection or via encapsulation within a scaffold for sustained release. To date, exosome therapy for cartilage injuries has yet to be optimized as the ideal cell source for exosomes, and the dose and method of delivery have yet to be identified. More importantly, a deeper understanding of the role of exosomal miRNAs in cartilage repair is paramount for the development of more effective exosome therapy.

Knee osteoarthritis: key treatments and implications for physical therapy

BACKGROUND

Knee osteoarthritis (OA) is a chronic progressive disease that imparts a substantial socioeconomic burden to society and healthcare systems. The prevalence of knee OA has dramatically risen in recent decades due to consistent increases in life expectancy and obesity worldwide. Patient education, physical exercise, and weight loss (for overweight or obese individuals) constitute the first-line knee OA treatment approach. However, less than 40% of patients with knee OA receive this kind of intervention. There is an unmet need for healthcare professionals treating individuals with knee OA to understand the current recommended treatment strategies to provide effective rehabilitation.

OBJECTIVE

To guide physical therapists in their clinical decision making by summarizing the safest and most efficacious treatment options currently available, and by delineating the most traditional outcome measures used in clinical research for knee OA.

CONCLUSION

There is a need for healthcare providers to abandon low-quality and ineffective treatments and educate themselves and their patients about the current best evidence-based practices for knee OA.