Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

Arbeeva, Liubov; Minnig, Mary Catherine; Yates, Katherine A; Nelson, Amanda

doi:10.1007/s11926-023-01114-9

Cited by 5 publications

References 47 publications

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Therapeutic Controlled Release Strategies for Human Osteoarthritis

Wang,

Liu,

Venkatesan

et al. 2024

Adv Healthcare Materials

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Osteoarthritis is a progressive, irreversible debilitating whole joint disease that affects millions of people worldwide. Despite the availability of various options (non‐pharmacological and pharmacological treatments and therapy, orthobiologics, and surgical interventions), none of them can definitively cure osteoarthritis in patients. Strategies based on the controlled release of therapeutic compounds via biocompatible materials may provide powerful tools to enhance the spatiotemporal delivery, expression, and activities of the candidate agents as a means to durably manage the pathological progression of osteoarthritis in the affected joints upon convenient intra‐articular (injectable) delivery while reducing their clearance, dissemination, or side effects. The goal of this review is to describe the current knowledge and advancements of controlled release to treat osteoarthritis, from basic principles to applications in vivo using therapeutic recombinant molecules and drugs and more innovatively gene sequences, providing a degree of confidence to manage the disease in patients in a close future.

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Therapeutic Controlled Release Strategies for Human Osteoarthritis

Wang,

Liu,

Venkatesan

et al. 2024

Adv Healthcare Materials

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Clinical phenotypes, molecular endotypes and theratypes in OA therapeutic development

Mobasheri,

Loeser

2024

Nat Rev Rheumatol

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Artificial intelligence in planned orthopaedic care

Georgiakakis,

Khan,

Logishetty

et al. 2024

SICOT-J

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The integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-making tools, optimisation of implant design, robotic surgery, and remote patient monitoring. Collectively, these advances propose to enhance patient care and improve system efficiency. Musculoskeletal pathologies represent the most significant contributor to global disability, with roughly 1.71 billion people afflicted, leading to an increasing volume of patients awaiting planned orthopaedic surgeries. This has exerted a considerable strain on healthcare systems globally, compounded by both the COVID-19 pandemic and the effects of an ageing population. Subsequently, patients face prolonged waiting times for surgery, with further deterioration and potentially poorer outcomes as a result. Furthermore, incorporating AI technologies into clinical practice could provide a means of addressing current and future service demands. This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care.

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Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

Cited by 5 publications

References 47 publications

Therapeutic Controlled Release Strategies for Human Osteoarthritis

Therapeutic Controlled Release Strategies for Human Osteoarthritis

Clinical phenotypes, molecular endotypes and theratypes in OA therapeutic development

Artificial intelligence in planned orthopaedic care

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