Predicting nonunion - a hybrid machine-learning model combining patient factors and mechanobiological simulation features exceeds clinician accuracy.

Aims: To compare the accuracy of a mechanobiological simulation against that of clinicians in forecasting long bone nonunions, and to determine whether the simulation's results can be effectively used as input for a machine-learning algorithm. Methods: This was a retrospective diagnostic accuracy study using a cohort of 98 patients who underwent surgery for tibial shaft fractures at four tertiary care trauma institutions in Germany between January 2018 and December 2023, 20 of whom developed nonunion. Biplanar pre- and postoperative radiographs and detailed demographic and clinical information (sex, age, BMI, comorbidities, fracture characteristics, surgical protocol, and implants used) were provided to clinicians to predict the likelihood of nonunion. All data were also analyzed with the Leeds-Genoa Non-Union-Index (LEG NUI) and entered into a mechanobiological simulation model, a machine-learning model, and a hybrid model. Performance metrics were then compared across all five assessment methods. Results: Comparing the overall predictive performance of the clinicians' heuristic, the LEG NUI score, and the three computational models, the hybrid model outperforms all contenders concerning sensitivity (80%), positive predictive value (59%), negative predictive value (94%), and Matthews correlation coefficient (59%). Specificity was high (86%), solely outperformed by the specificity of the LEG NUI score (95%) and the mechanobiological simulation (90%). Conclusion: The AI-based hybrid model outperforms human experts in predicting nonunions in long bone fractures, enabling the early detection of patients at risk and the modification of treatment strategies to improve patient outcomes.