MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays

Osteoporosis is underdiagnosed because dual-energy X-ray absorptiometry (DXA) is costly and scarce. We present MultiScaleKANNet, a hybrid deep-learning architecture for radiographic bone-loss risk stratification from routine knee X-rays, combining convolutional feature learning, learnable nonlinear transformations via Kolmogorov-Arnold Network (KAN) layers, and Transformer-based multi-scale attention. We evaluated MultiScaleKANNet on 2,035 knee radiographs from four public Kaggle sources with three categories (Healthy, Osteopenia, Osteoporosis). The labels are proxy labels-some derived from quantitative ultrasound T-scores rather than DXA-so results represent radiographic risk stratification, not clinical diagnosis. On a stratified held-out test set ([Formula: see text]), the model achieved 97.30% accuracy (95% CI: 95.3-98.6%; Cohen's [Formula: see text]; MCC[Formula: see text]; micro-averaged AUC[Formula: see text]). A source-held-out evaluation yielded 89.52% binary accuracy ([Formula: see text]), suggesting in-distribution metrics may partly reflect dataset homogeneity. Ablation studies confirm synergistic gains from KAN layers (+2.46%), multi-scale processing (+4.17%), and Transformer attention (+4.91%), with 40% parameter reduction versus ResNet-18. This is a methodological feasibility study; prospective DXA-confirmed validation is required.