Phenotyping TPF via Self-Supervised Learning: A Label-Agnostic Framework with Expert Validation

• URL: http://arxiv.org/abs/2606.17295v1
• Date: Mon, 15 Jun 2026 21:04:31 GMT
• Title: Phenotyping TPF via Self-Supervised Learning: A Label-Agnostic Framework with Expert Validation
• Abstract summary: We design, implement, and validate a label-agnostic framework to learn fracture representations directly from imaging data without observer-assigned labels.<n>A RadImageNet-pretrained ResNet-50 encoder is fine-tuned on 154 cleaned knee radiographs.<n>We show that label-agnostic SSL phenotyping is a reproducible and clinically interpretable complement to conventional classification.
• Score: 2.201241345037463
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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