1. Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery
- Author
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Donié, Cedric, Reumann, Marie K., Hartung, Tony, Braun, Benedikt J., Histing, Tina, Endo, Satoshi, and Hirche, Sandra
- Subjects
Computer Science - Machine Learning ,J.3 ,I.5.4 - Abstract
Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union. The models provided prediction results with 70% sensitivity, and the specificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol., Comment: To be presented at the 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2024)
- Published
- 2024