Abstract
Research Type:
Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies
Introduction/Purpose:
Occult posterior malleolar fractures (PMFs) are reported commonly in tibial shaft fractures. Presurgical identification is necessary to negate possible complications during tibial fracture surgical treatment (e.g. PMF fracture displacement. The aims of this study was to determine the most relevant factors predicting PM fractures by applying the AI "Minimum Redundancy Maximum Relevance" (mRMR) feature selection method to investigate the predictive power of various clinical and demographic factors.
Methods:
This was a historic cohort study, employing the mRMR method to identify the most relevant features associated with occult PMF in tibial shaft fractures . The inclusion criteria for this study were any patient who had sustained a diaphyseal tibial fracture who had undergone surgery during the study period who had also undergone a CT scan in addition to plain radiographs. The selected features were then used to train a machine learning model for identifying occult PMF. The model's performance was evaluated by measuring classification accuracy across different combinations of features.
Results:
Out of 764 diaphyseal fractures identified 442 met the inclusion criteria. A total of 107 patients had PMF extensions (24.21%). The analysis revealed that tibia the most relevant fractures were tibia fracture type, fibular fracture morphology, tibia fracture level, fibular fracture level and mechanism. On further analysis, tibial spiral fracture was the most significant predictor, with a classification accuracy of 0.78 with other factor clusters not reaching the same significance. Low energy mechanisms and tibial fracture comminution were the best predictors of no occult PMF.
Conclusion:
This study demonstrates that AI can effectively be used to identify prediction factors of occult PMF in tibial fractures. Spiral tibia fractures being the most relevant predictor. The findings highlight the potential of using AI-driven methods, such as mRMR, to enhance the accuracy of injury prediction, and these findings are in keeping with results from traditional statistics.