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Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity
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DOI：10.3389/frai.2026.1909177.png)
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Abstract
En 中文
Artificial intelligence (AI) is reshaping fracture diagnosis in medical imaging. Despite these advances; accurately identifying atypical fractures (such as stress or pathological fractures) and complex fractures (including comminuted and pelvic fractures) remains a significant clinical challenge. This systematic review evaluates the current evidence on AI models; including advanced architectures; for detecting; classifying; and segmenting atypical and complex bone fractures in humans. A total of 40 studies published between 2015 and 2026 met the predefined inclusion criteria. Eligible studies used real-world imaging modalities (X-ray; CT; or MRI); focused on atypical or complex fractures; employed AI-based approaches with expert-validated reference standards; and reported quantitative performance metrics. Studies based exclusively on synthetic data; restricted to simple fractures; or lacking adequate validation were excluded. Advanced AI models; including hybrid frameworks such as 3D U-Net variants and DeepLabV3+MobileNetV3; were associated with improved performance in several studies; particularly for identifying subtle and multi-fragment fractures. However; substantial heterogeneity in study design; datasets; validation strategies; and evaluation metrics limits direct comparisons across models. Hybrid systems; particularly CNN-based architectures combined with level-set methods or multi-network pipelines; also appeared effective in capturing complex fracture patterns in several studies; although this observation is based on a limited and heterogeneous body of evidence. Overall; the available evidence suggests that advanced AI models have considerable potential to improve the detection; classification; and segmentation of atypical and complex fractures. Nevertheless; the predominance of single-center studies; the limited use of external or prospective validation; and methodological heterogeneity indicate that further standardized; multicenter clinical validation is required before these models can be widely implemented in routine clinical practice.
Keywords:
machine learning
AI
medical imaging
detection
atypical fractures
complex fractures
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