The full potential of artificial intelligence in tibial plateau fracture characterisation remains unrealised, constrained by a fundamental dependency on labelled datasets whose consistency cannot be guaranteed: conventional classification schemes such as Schatzker and AO/OTA suffer from inter-observer variability, causing supervised models to learn human disagreement rather than stable fracture morphology. We design, implement, and validate a label-agnostic framework that eliminates this constraint by learning fracture representations directly from imaging data without observer-assigned labels. A RadImageNet-pretrained ResNet-50 encoder is fine-tuned on 154 cleaned knee radiographs using the SimCLR contrastive objective, preceded by a data cleaning protocol and followed by UMAP dimensionality reduction and k-means clustering to discover four imaging-derived phenotypes. Phenotype validity is assessed through a blinded expert review protocol administered to two independent clinicians. The four phenotypes demonstrate robust stability (bootstrap ARI = 0.319 +/- 0.041), strong internal cohesion (silhouette = 0.511), and coherence ratings of 3-5/5 from both reviewers under blinded conditions; one phenotype was unanimously identified as exhibiting comminution -- a high-complexity feature isolated without any supervisory signal. Inter-partition comparison against Schatzker labels yields ARI = 0.013, confirming orthogonality to conventional classification boundaries. Notably, expert reviewers anchored to established classification vocabularies perceived imaging-derived groups as heterogeneous precisely where Schatzker alignment was lowest, suggesting that Schatzker-trained perception and label-agnostic embedding geometry measure orthogonal dimensions. These findings establish label-agnostic SSL phenotyping as a reproducible and clinically interpretable complement to conventional classification.

翻译：人工智能在胫骨平台骨折特征分析中的潜力尚未完全发挥，其根本制约在于对标注数据集的高度依赖——而这类数据的一致性难以保障：Schatzker与AO/OTA等传统分类方案存在观察者间差异，导致监督模型学习到的是人类分歧而非稳定的骨折形态。本研究设计、实现并验证了一个无标注框架，通过直接从影像数据学习骨折表征（无需观察者标注）来消除这一制约。采用RadImageNet预训练的ResNet-50编码器，在154张经过清洗的膝关节X光片上通过SimCLR对比学习目标进行微调，前期执行数据清洗流程，后续经UMAP降维与k-means聚类，最终发现四种源自影像的表型。通过双盲专家审核方案（由两名独立临床医生执行）评估表型有效性。四种表型展现出稳健的稳定性（bootstrap ARI=0.319±0.041）、强内部凝聚性（轮廓系数=0.511），两名审核者在盲态下均给出3-5/5的连贯性评分；其中一种表型被一致认定为呈现粉碎性特征——这是一种在无任何监督信号下分离出的高复杂度征象。与Schatzker标签的跨分区比较显示ARI=0.013，证实其与传统分类边界正交。值得注意的是，锚定于既定分类词汇体系的专家审核者，恰在Schatzker对齐度最低处将影像衍生组群判定为异质性，表明Schatzker训练的感知与无标注嵌入几何结构测量的是正交维度。这些发现确立了无标注自监督学习表型分析作为传统分类可重复且临床可解释的补充方法。