knee-implant-detector

Knee implant detector

Binary classifier that flags total-knee-replacement hardware in pre-cropped knee radiograph regions. Used as a preprocessing gate for DeepKnee: knees with an implant are discarded instead of KL-graded.

• Architecture: torchvision mobilenet_v3_small (2.5M params), single-logit head, 256x256 input, ImageNet normalization.
• Checkpoint format: plain state_dict + metadata (arch, input size, normalization, two operating thresholds). No pickle of custom classes - loads with torch >= 1.6 and torchvision >= 0.9.
• Training data: OAI post-replacement knees (254 positives) vs OAI implant-free knee crops (10,129 negatives), patient-grouped 5-fold split.
• Inference preprocessing: per-crop 5/99-percentile contrast normalization (built into infer_implant.py); robust to input brightness and 8/16-bit depth.

Results

Pooled 5-fold out-of-fold: PR-AUC 0.9886, ROC-AUC 0.9998. Hold-out fold 0 via the shipped inference code: PR-AUC 0.9988, sens 51/51, spec 0.9985.

Stored operating points:

Usage

from infer_implant import load_model, predict_image, has_implant

model, meta = load_model()  # downloads this checkpoint (or pass a path)
prob = predict_image(model, meta, "knee_crop.png")
discard = has_implant(model, meta, "knee_crop.png")  # sens99 threshold

infer_implant.py lives in the KneePilot repo (experiements/implant_detector/); it depends only on torch, torchvision, numpy and Pillow, and runs on CPU or GPU.