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, Hirche, Sandra, Donié, Cedric, Reumann, Marie K., Hartung, Tony, Braun, Benedikt J., Histing, Tina, Endo, Satoshi, and Hirche, Sandra more...
- 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) more...
- Published
- 2024