Key result
This survey highlights the current achievements and future research avenues for utilizing machine learning and wearable sensors to predict, evaluate, and manage Total Joint Replacement outcomes.
Why the study?
Machine learning and wearable sensors have seen limited contribution to predicting, evaluating, and managing total joint replacement outcomes, and findings from these studies had not been summarized.
How are wearable sensors and machine learning methods used in predicting, evaluating, and managing Total Joint Replacement treatment outcomes?
How are wearable sensors and machine learning methods used in predicting, evaluating, and managing Total Joint Replacement treatment outcomes?
Answers are grounded in this paper and cite their sources.
This survey summarizes the current applications and future research avenues for using wearable sensors and machine learning to improve total joint replacement outcomes.
Total Joint Replacement (TJR) surgeries are one of the most prevalent operations that are undergone by the elderly population. With the world population aging, the number of surgeries will continue to increase. A small portion of these surgeries result in complications that require readmissions. These readmissions amount to a significant financial and time burden for both the patients and the hospitals. In the past decade machine learning and wearable sensors have both been used extensively in the healthcare domain but the contribution to the prediction/evaluation and management of TJR is limited. What’s more, to our best knowledge there has been no effort in summarizing the findings from these studies. Therefore, this study highlights what has been achieved by using machine learning and wearable sensors in the TJR context and point out possible research avenues.
No takes yet. Share an insight, caveat, or question.
Yerebakan et al. (2020) conducted a review in Total Joint Replacement (TJR). Wearable sensors and machine learning methods was evaluated. This survey highlights the current achievements and future research avenues for utilizing machine learning and wearable sensors to predict, evaluate, and manage Total Joint Replacement outcomes.
Synapse has enriched 10 related papers. Consider them for comparative context: