Abstract
Tibial plateau fractures account for approximately 1% of skeletal fractures, with treatment strategies varying based on fracture type, displacement, and articular involvement. Diagnosis is labor-intensive, time-consuming, repetitive, and subject to considerable inter-observer variability. Automated and precise approaches could improve accuracy and efficiency in fracture severity classification. With advances in artificial intelligence (AI), especially deep learning, such techniques are increasingly applied in medicine, yet their performance depends on high-quality training data. Here, we present a first-of-its-kind open-access dataset for AI-based analysis of tibial plateau fractures. The dataset comprises 421 heterogeneous anterior-posterior radiographs from 186 patients (mean age 45.88 ± 17.54 years; 37 females, 149 males), including normal and fractured knees. Fractures were classified by expert orthopedic surgeons and radiologists using the Schatzker system: type I (14.51%), II (18.27%), III (6.45%), IV (5.91%), V (6.45%), VI (17.20%), and normal (31.18%). All images were segmented to generate tibial bone masks, supporting morphological analysis, AI training, and automated fracture assessment. This dataset facilitates AI-driven fracture detection, classification, preoperative planning, and orthopedic assistant education.
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Data availability
The dataset utilized in this study is publicly available in the cloud and can be accessed on Zenodo (https://doi.org/10.5281/zenodo.18007397)18 under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. All data were anonymized and prepared following ethical standards. Researchers are encouraged to use the dataset for academic and non-commercial research purposes.
Code availability
The dataset provides patient-specific data in MATLAB .mat files, each containing structured information for tibial plateau fracture images and segmentation masks. The MATLAB and Python scripts for reading, processing, and visualizing the data are available at the following repository: https://github.com/ali-kazemi8/PlaTiF-Tibial-Plateau-Fracture-Dataset.git.
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A.K.: Conceptualization, Project Administration, Investigation, Data Curation, Methodology, Writing – Original Draft & Editing. K.S.: Writing – Original Draft, Formal Analysis. A.Z.: Image segmentation, Investigation, Writing – Original Draft. S.E.: Clinical Expertise for Validation, Data Annotation. E.N.: Clinical Expertise for Validation, Writing – Review & Editing. A.A.: Supervision, Writing – Review & Editing. P.F.: Supervision, Conceptualization, Writing – Review & Editing. M.H.N.: Supervision, Clinical Expertise for Validation, Writing – Review & Editing. All authors read and approved the final manuscript.
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Kazemi, A., Same, K., Zamanirad, A. et al. PlaTiF: A pioneering dataset for orthopedic insights in AI-powered diagnosis of tibial plateau fractures. Sci Data (2026). https://doi.org/10.1038/s41597-026-06560-5
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DOI: https://doi.org/10.1038/s41597-026-06560-5
