evaluation of the schatzker-Kfuri Classification of Tibial Plateau Fractures using Radiographs and Computed Tomography: Comparison Between expert Observer and the ChatGPT-4o model
Abstract
Introduction: Artificial intelligence was formally introduced in 1956, and since then, platforms trained on large datasets have been developed to generate increasingly accurate outputs. The Kfuri-Schatzker classification of tibial plateau fractures enables more precise analysis, particularly when CT imaging is integrated. This study compared the diagnostic accuracy of the ChatGPT-4o model with that of expert evaluators. Materials and Methods: A retrospective observational study was conducted to compare the interpretations of an expert observer with those generated by ChatGPT-4o. A dataset of 45 expert-published case reports including radiographs and CT scans from databases such as PubMed, Elsevier, and SciELO was used to refine the prompt guiding ChatGPT-4o’s analysis. Six additional case reports of tibial plateau fractures, none previously provided to the model, were selected for evaluation. ChatGPT-4o analyzed each case and proposed a classification according to the Schatzker-Kfuri system. Its responses were compared with the expert diagnoses reported in the literature. Results: ChatGPT-4o correctly classified all the cases analyzed. In bicondylar fractures, the model accurately identified components of subsidence, shear (split) pattern, and epiphyseal-diaphyseal dissociation. Cohen’s kappa coefficient was 1.00, indicating perfect agreement. Conclusion: The ChatGPT-4o model demonstrated high diagnostic accuracy in classifying tibial plateau fractures using the Schatzker-Kfuri system, achieving agreement comparable to that of an expert evaluator.Downloads
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