The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".