The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review
Background: Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a common and serious complication following lower extremity arthroplasty, such as total hip and knee arthroplasty. Due to the increasing number of these surgeries, accurately predicting VTE risk is cr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Arthroplasty Today |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352344125000597 |
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| Summary: | Background: Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a common and serious complication following lower extremity arthroplasty, such as total hip and knee arthroplasty. Due to the increasing number of these surgeries, accurately predicting VTE risk is crucial. Traditional clinical prediction models often fall short due to their complexity and limited accuracy. Methods: This Preferred Reporting Items for Systematic Review and Meta-Analyses–guided systematic review summarized the application of artificial intelligence (AI) and machine learning models in predicting VTE after total joint arthroplasty. Databases including PubMed, Scopus, Web of Science, and Embase were searched for relevant studies published up to January 2024. Eligible studies focused on the predictive accuracy of AI algorithms for VTE post arthroplasty and were assessed for quality using the Newcastle-Ottawa Scale. Results: A total of 7 retrospective cohort studies, encompassing 579,454 patients, met the inclusion criteria. These studies primarily employed the extreme gradient boosting model, which generally demonstrated strong predictive performance with area under the curve values ranging from 0.71 to 0.982. Models like random forest and support vector machines also performed well. However, only 1 study included external validation, critical for assessing generalizability. Conclusions: AI and machine learning models, particularly extreme gradient boosting, exhibit significant potential in predicting VTE after lower extremity arthroplasty, outperforming traditional clinical prediction tools. Yet, the need for external validation and high-quality, generalizable datasets remains critical before these models can be widely implemented in clinical practice. The study underscores the role of AI in preoperative planning to enhance patient outcomes in orthopaedic surgery. |
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| ISSN: | 2352-3441 |