evaluation of the schatzker-Kfuri Classification of Tibial Plateau Fractures using Radiographs and Computed Tomography: Comparison Between expert Observer and the ChatGPT-4o model
Article Sidebar
Published:
Dec 27, 2025
Keywords:
Artificial intelligence, tibial plateau, Schatzker-Kfuri classification
Main Article Content
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.
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
Download data is not yet available.
Article Details
How to Cite
Rivadeneira Jurado, H. A., Rivadeneira Jurado, E. A., Espinoza Freire, D., Samaniego, A. F., Lulkin, E., Bidolegui, F., & Pereira, S. (2025). evaluation of the schatzker-Kfuri Classification of Tibial Plateau Fractures using Radiographs and Computed Tomography: Comparison Between expert Observer and the ChatGPT-4o model. Revista De La Asociación Argentina De Ortopedia Y Traumatología, 90(6), 556-560. https://doi.org/10.15417/issn.1852-7434.2025.90.6.2224
Issue
Section
Clinical Research
Copyright (c) 2025 Revista de la Asociación Argentina de Ortopedia y Traumatología
Manuscript acceptance by the Journal implies the simultaneous non-submission to any other journal or publishing house. The RAAOT is under the Licencia Creative Commnos Atribución-NoComercial-Compartir Obras Derivadas Igual 4.0 Internacional (CC-BY-NC.SA 4.0) (http://creativecommons.org/licences/by-nc-sa/4.0/deed.es). Articles can be shared, copied, distributed, modified, altered, transformed into a derivative work, executed and publicly communicated, provided a) the authors and the original publication (Journal, Publisher and URL) are mentioned, b) they are not used for commercial purposes, c) the same terms of the license are maintained.
In the event that the manuscript is approved for its next publication, the authors retain the copyright and will assign to the journal the rights of publication, edition, reproduction, distribution, exhibition and communication at a national and international level in the different databases. data, repositories and portals.
It is hereby stated that the mentioned manuscript has not been published and that it is not being printed in any other national or foreign journal.
The authors hereby accept the necessary modifications, suggested by the reviewers, in order to adapt the manuscript to the style and publication rules of this Journal.
References
1. Lhotská L. Umělá inteligence v medicíně a zdravotnictví: Příležitost a/nebo hrozba? Čas Lék Čes
2023;162(7-8):275-8. Disponible en: https://www.prolekare.cz/casopisy/casopis-lekaru-ceskych/2023-7-8-1/umela-inteligence-v-medicine-a-zdravotnictvi-prilezitost-a-nebo-hrozba-136669
2. Mucci T. La historia de la inteligencia artificial. IBM Think 2019 [citado 2025 nov 21]. Disponible en: https://www.ibm.com/es-es/think/topics/history-of-artificial-intelligence.
3. Kfuri M, Schatzker J. Revisiting the Schatzker classification of tibial plateau fractures. Injury 2018;49(12):2252-63. https://doi.org/10.1016/j.injury.2018.07.010
4. Mohammadi M, Parviz S, Parvaz P, Pirmoradi MM, Afzalimoghaddam M, Mirfazaelian H. Diagnostic performance of ChatGPT in tibial plateau fracture in knee X-ray. Emerg Radiol 2025;32(1):59-64. https://doi.org/10.1007/s10140-024-02298-y
5. Van der Gaast N, Bagave P, Assink N, Broos S, Jaarsma RL, Edwards MJR, et al. Deep learning for tibial plateau fracture detection and classification. Knee 2025;54:81-9. https://doi.org/10.1016/j.knee.2025.02.001
6. Markhardt B, Gross JM, Monu J. Schatzker classification of tibial plateau fractures: Use of CT and MR imaging improves assessment. Radiographics 2009;29(2):585-97. https://doi.org/10.1148/rg.292085078
7. Gyftopoulos S, Lin D, Knoll F, Doshi AM, Cantarelli Rodrigues T, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol 2019;213(3):506-13. https://doi.org/10.2214/AJR.19.21117
8. Kuo R, Harrison C, Curran T, Jones B, Freethy A, Cussons D, et al. Artificial intelligence in fracture detection: A
systematic review and meta-Analysis. Radiology 2022;304(1):50-62. https://doi.org/10.1148/radiol.211785
9. Giordano V, Schatzker J, Kfuri M. The ‘Hoop’ plate for posterior bicondylar shear tibial plateau fractures:
Description of a new surgical technique. J Knee Surg 2022;35(2):123-9. https://doi.org/10.1055/s-0036-1593366
10. Singh Sidhu GA, Hind J, Ashwood N, Kaur H, Bridgwater H, Rajagopalan S. A systematic review of current approaches to tibial plateau, Cureus 2022;14(7):e27183. https://doi.org/10.7759/cureus.27183
11. Cai D, Zhou Y, He W, Yuan J, Liu C, Li R, et al. Automatic segmentation of knee CT images of tibial plateau fractures based on three-dimensional U-Net: assisting junior physicians with Schatzker classification. Eur J Radiol 2024;178:111605. https://doi.org/10.1016/j.ejrad.2024.111605
12. Liu Y, Fang R, Tu B, Zhu Z, Zhang C, Ning R. Correlation of preoperative CT imaging shift parameters of the lateral plateau with lateral meniscal injury in Schatzker IV-C tibial plateau fractures. BMC Musculoskelet Disord 2023;24(1):793. https://doi.org/10.1186/s12891-023-06924-7
13. Martinez A, Cayon M. Fracturas del platillo tibial posterior. Revista Colombiana de Cirugía Ortopédica y Traumatología 1999;13(1):37-1. Disponible en: https://sccot.org/pdf/RevistaDigital/1999/Vol13N1/37-41.pdf
14. De Cicco F, Verbner J, Abrego M, Taype D, Carabelli G, Barla J, et al. Soporte circunferencial posterior en fracturas de platillo tibial. Rev Asoc Argent Ortop Traumatol 2021;86(2):219-27. https://doi.org/10.15417/issn.1852-7434.2021.86.2.1018
15. Alenazi HK, Alahmari RA, Mubarak Hassan Al Faraj A, Nasser Almurkan M, Saleh Al Hashel IM, Al Hagwi AI, et al. The future of artificial intelligence in X-ray radiography: Enhancing healthcare and workflow efficiency. J Int
Crisis Risk Commun Res 2024;7(53):51-3. https://doi.org/10.63278/jicrcr.vi.708
2023;162(7-8):275-8. Disponible en: https://www.prolekare.cz/casopisy/casopis-lekaru-ceskych/2023-7-8-1/umela-inteligence-v-medicine-a-zdravotnictvi-prilezitost-a-nebo-hrozba-136669
2. Mucci T. La historia de la inteligencia artificial. IBM Think 2019 [citado 2025 nov 21]. Disponible en: https://www.ibm.com/es-es/think/topics/history-of-artificial-intelligence.
3. Kfuri M, Schatzker J. Revisiting the Schatzker classification of tibial plateau fractures. Injury 2018;49(12):2252-63. https://doi.org/10.1016/j.injury.2018.07.010
4. Mohammadi M, Parviz S, Parvaz P, Pirmoradi MM, Afzalimoghaddam M, Mirfazaelian H. Diagnostic performance of ChatGPT in tibial plateau fracture in knee X-ray. Emerg Radiol 2025;32(1):59-64. https://doi.org/10.1007/s10140-024-02298-y
5. Van der Gaast N, Bagave P, Assink N, Broos S, Jaarsma RL, Edwards MJR, et al. Deep learning for tibial plateau fracture detection and classification. Knee 2025;54:81-9. https://doi.org/10.1016/j.knee.2025.02.001
6. Markhardt B, Gross JM, Monu J. Schatzker classification of tibial plateau fractures: Use of CT and MR imaging improves assessment. Radiographics 2009;29(2):585-97. https://doi.org/10.1148/rg.292085078
7. Gyftopoulos S, Lin D, Knoll F, Doshi AM, Cantarelli Rodrigues T, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol 2019;213(3):506-13. https://doi.org/10.2214/AJR.19.21117
8. Kuo R, Harrison C, Curran T, Jones B, Freethy A, Cussons D, et al. Artificial intelligence in fracture detection: A
systematic review and meta-Analysis. Radiology 2022;304(1):50-62. https://doi.org/10.1148/radiol.211785
9. Giordano V, Schatzker J, Kfuri M. The ‘Hoop’ plate for posterior bicondylar shear tibial plateau fractures:
Description of a new surgical technique. J Knee Surg 2022;35(2):123-9. https://doi.org/10.1055/s-0036-1593366
10. Singh Sidhu GA, Hind J, Ashwood N, Kaur H, Bridgwater H, Rajagopalan S. A systematic review of current approaches to tibial plateau, Cureus 2022;14(7):e27183. https://doi.org/10.7759/cureus.27183
11. Cai D, Zhou Y, He W, Yuan J, Liu C, Li R, et al. Automatic segmentation of knee CT images of tibial plateau fractures based on three-dimensional U-Net: assisting junior physicians with Schatzker classification. Eur J Radiol 2024;178:111605. https://doi.org/10.1016/j.ejrad.2024.111605
12. Liu Y, Fang R, Tu B, Zhu Z, Zhang C, Ning R. Correlation of preoperative CT imaging shift parameters of the lateral plateau with lateral meniscal injury in Schatzker IV-C tibial plateau fractures. BMC Musculoskelet Disord 2023;24(1):793. https://doi.org/10.1186/s12891-023-06924-7
13. Martinez A, Cayon M. Fracturas del platillo tibial posterior. Revista Colombiana de Cirugía Ortopédica y Traumatología 1999;13(1):37-1. Disponible en: https://sccot.org/pdf/RevistaDigital/1999/Vol13N1/37-41.pdf
14. De Cicco F, Verbner J, Abrego M, Taype D, Carabelli G, Barla J, et al. Soporte circunferencial posterior en fracturas de platillo tibial. Rev Asoc Argent Ortop Traumatol 2021;86(2):219-27. https://doi.org/10.15417/issn.1852-7434.2021.86.2.1018
15. Alenazi HK, Alahmari RA, Mubarak Hassan Al Faraj A, Nasser Almurkan M, Saleh Al Hashel IM, Al Hagwi AI, et al. The future of artificial intelligence in X-ray radiography: Enhancing healthcare and workflow efficiency. J Int
Crisis Risk Commun Res 2024;7(53):51-3. https://doi.org/10.63278/jicrcr.vi.708