OrthoScan AI: A Unified, Interpretable Deep-Learning Pipeline for Scoliosis Severity and Knee Osteoarthritis Grading from Plain Radiographs on Commodity CPUs
Year: 2026
Type: article
Abstract: Scoliosis and knee osteoarthritis (OA) are among the most frequently radiographed musculoskeletal conditions, yet both are still graded by eye: the Cobb angle for spinal curvature and the Kellgren–Lawrence (KL) scale for knee OA carry well-documented inter-observer variability, and specialist readers are scarce outside large hospitals. We present OrthoScan AI, a single screening pipeline that accepts an unlabeled radiograph, decides automatically whether it shows a spine or a knee, and returns a clinically anchored severity output—a Cobb-angle band with an angle estimate, or a KL grade—together with a Grad-CAM explanation, on a CPU-only server (0.3–0.4 s per image without, and 0.7–1.2 s with, the explanation on a 12-core laptop). The two diagnostic branches share one preprocessing chain (border cropping, CLAHE, edge-preserving denoising and letterboxing) and one transfer-learning recipe (frozen-trunk warm-up, discriminative learning rates with cosine decay, class-balanced sampling, label smoothing and early stopping on balanced accuracy). We evaluate on three public datasets. On the Osteoarthritis-Initiative-derived KL-graded knee set of Chen et al. (8,260 images, fixed split) a single DenseNet-121 reaches 62.3 ± 0.6% five-class accuracy, 67.0 ± 0.9% balanced accuracy and a quadratic-weighted κ of 0.79 over three seeds, with 94% of predictions within one grade and KL-4 recall of 0.88; a pre-declared soft-vote ensemble of the nine trained knee networks raises this to 67.6% accuracy, 71.0% balanced accuracy and κ = 0.84 (McNemar p < 10−4 ), and an ablation with paired tests shows that balanced sampling alone recovers minority-grade recall while stacking it with loss re-weighting over-corrects. On 338 real clinical spine radiographs the system detects scoliosis with 96.8% sensitivity (pooled five-fold accuracy 95.9%, AUC 0.986). On the 20,000-image Spinal-AI2024 synthetic set the learned angle head estimates the major Cobb angle with a mean absolute error of 2.6◦ (89% within 5◦ ), whereas the weights-free geometric tracer the system uses as a confidence-gated fallback reaches only 11.6◦ , so we characterise it as a coarse screening signal rather than a measurement. Robustness to compression, noise, blur and exposure, Grad-CAM plausibility and per-stage latency are reported. Eval
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225587872