Key result
Machine learning beats experienced clinician judgment with ~62% accuracy for predicting post-extraction bleeding on antithrombotics.
Why the study?
The study aimed to identify high-risk dental extractions in patients taking antiplatelet medication or anticoagulants and to compare an experienced surgeon's decisions with machine learning algorithms.
Do machine learning algorithms improve the prediction of postoperative bleeding risk compared to clinical experience in patients on antithrombotic medication undergoing dental extractions?
Observational (n=2,000)
No
Do machine learning algorithms improve the prediction of postoperative bleeding risk compared to clinical experience in patients on antithrombotic medication undergoing dental extractions?
Absolute Event Rate: 62% vs 53%
Machine learning algorithms provide a more accurate and objective assessment of postoperative bleeding risk than clinical experience alone in patients on antithrombotics undergoing dental extractions.
ML algorithms may aid risk stratification for dental extractions on antithrombotics; leaves open whether they improve decisions over experienced surgeons in prospective use.
OBJECTIVES: The aim of this study was to identify high-risk dental extractions in patients taking antiplatelet (AP) medication or anticoagulants (ACs) and to compare an experienced surgeon's decisions with machine learning (ML) algorithms. MATERIALS AND METHODS: Our study included 2000 procedures, of which 1788 were conducted in patients under monotherapy with AP medication, vitamin K antagonists (VKAs), heparin, or direct oral anticoagulants (DOACs), 426 were performed under dual therapy, and 27 under triple therapy. Four algorithms, logistic regression (LR), eXtreme gradient boost (XGB), random forest (RF), and K-nearest neighbors (KNN), were trained with 80% (1600 procedures) of the derived data. Afterwards, an experienced oral surgeon and the algorithms were tested on the remaining 20% (400 procedures) of the data to evaluate the predictive power with respect to bleeding incidents. RESULTS: The incidence of hemorrhagic events was low (4.35%). Dual anticoagulation significantly affected the risk of bleeding. Evaluating the results of the predictions, all four algorithms outperformed the surgeon in terms of balanced accuracy (LR: 58%; RF: 59%; XGB: 61%; KNN: 62%; surgeon: 53%). CONCLUSIONS: Decision-making based on various parameters influencing bleeding risk is complex, and surgeons tend to overestimate this risk. Both the algorithms and the surgeon had a share of false positive predictions; however, in a medical context, preventive overcaution does less damage than underestimation. CLINICAL RELEVANCE: Algorithms can provide an objective assessment of bleeding risk and help determine risk profiles, uncover variables with the highest predictive power, and serve as guidance on postoperative observation periods. TRIAL REGISTRATION: This study was approved by the Ethics Committee of the Medical Faculty of RWTH Aachen (Decision Number 24-353). This was a retrospective clinical study designed to analyze postoperative bleeding after dental extractions in patients under antithrombotic medication and to evaluate the prediction of bleeding events by different algorithms and human experience.
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Katz et al. (2025) conducted an observational in Patients on antithrombotic medication undergoing dental extractions (n=2,000). Machine learning algorithms (LR, RF, XGB, KNN) vs. Experienced oral surgeon was evaluated on Balanced accuracy for predicting postoperative bleeding. Machine learning algorithms, particularly K-nearest neighbors (62% balanced accuracy), outperformed an experienced oral surgeon (53% balanced accuracy) in predicting postoperative bleeding after dental extractions in patients on antithrombotic medication.
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