Identify knee replacement implant manufacturers and model systems from AP and Lateral X-ray images using AI-assisted image analysis with Grad-CAM visual explainability.
ImplantDetect processes standard knee radiographs to identify prosthesis manufacturers and specific component lines using AI-assisted visual feature analysis. Designed as an educational and pre-operative revision research tool, the system provides transparent candidate rankings paired with explainability overlays.
Compatible with standard orthopedic imaging formats and DICOM medical imaging standards.
PNG, JPEG, JPG, and WEBP image files with automatic client-side preview.
Direct 16-bit DICOM parsing with VOI LUT windowing and contrast normalization.
Optimized for standard Anteroposterior (AP) and Lateral knee radiographic views.
Generous upload size threshold with automated magic-byte file signature validation.
ImplantDetect generates dual visual explainability outputs so clinicians can inspect the specific image regions driving candidate identification.
Gradient-weighted Class Activation Mapping computes gradient weights at the final convolutional layer to project a visual heatmap overlay highlighting condylar, stem, or tray features.
Automated ROI extraction isolates a focused 224×224 bounding crop centered around the highest activation hotspot with anatomical padding for detailed visual inspection.
The identification engine is configured to recognize 10 major knee prosthesis lines across leading orthopedic manufacturers.
Export complete analysis cases into standardized PDF summary reports containing side-by-side radiograph views, Grad-CAM heatmap overlays, top-3 confidence rankings, and timestamped case IDs.
ImplantDetect is developed and deployed strictly as an image-analysis technology platform for educational, evaluation, and pre-operative revision research purposes. It is not intended for primary clinical diagnosis or commercial decision-making.
All automated visual predictions and candidate probability scores must be independently cross-referenced and verified against patient surgical history, operative notes, and physical manufacturer implant records by qualified medical professionals.
Empowering orthopedic surgeons and imaging researchers with transparent, explainable deep learning insights.
Deep neural network classifies major orthopedic knee prosthesis manufacturers and model lines from AP and Lateral radiographs.
Returns top-3 candidate implant rankings with probability confidence percentages to support research review.
Visual gradient heatmaps highlight the specific condylar, stem, or tray features driving the AI classification decision.
Direct ingestion of raw 16-bit DICOM (.dcm) files with automatic VOI LUT windowing and contrast normalization.
Generate downloadable PDF summary reports containing side-by-side radiograph comparisons and timestamped metrics.
Built with Flask, OpenCV, PyDICOM, and containerized with Docker for deployment across research environments.
A 6-step deep learning pipeline designed to simplify pre-operative revision planning.
Upload standard AP or Lateral knee X-ray images in PNG, JPG, WEBP, or medical DICOM (.dcm) formats directly via drag-and-drop or camera capture.
Everything you need to know about the technology and clinical workflow.
ImplantDetect is optimized for standard Anteroposterior (AP) and Lateral knee radiographs. Scans should be well-centered around the knee joint for clear prosthesis visualization.
Uploaded medical DICOM (.dcm) files are ingested, parsed for 16-bit pixel data, and processed through Value of Interest (VOI) LUT windowing and contrast normalization before deep learning inference.
Grad-CAM (Gradient-weighted Class Activation Mapping) calculates gradients at the final convolutional layer to project a visual heatmap overlay highlighting image regions that contributed most strongly to the prediction. It functions as an explainability aid for clinical verification and is not a definitive diagnosis.
The current model supports identification across 10 major knee prosthesis lines:
The confidence score displayed alongside each result reflects statistical AI model probability based on learned visual features, and should always be evaluated alongside comprehensive clinical judgement rather than interpreted as absolute certainty.
All uploaded radiographs and analysis results are strictly isolated to your authenticated clinician account, stored in protected storage, and guarded by per-user access control.
The Grad-CAM Region of Interest (ROI) view provides a focused, cropped zoom centered directly around the strongest activation hotspot with surrounding anatomical context padding, distinguishing it from the full-field Grad-CAM heatmap overlay.
Yes. Authenticated clinicians can access their complete Case History to search, filter by manufacturer or confidence threshold, and review or download PDF reports for past analysis records at any time.
No. Per-user ownership rules strictly restrict media file and case record access to the clinician who uploaded them, with system administrator privileges reserved strictly for operational auditing.
If you encounter an unexpected result, missing model class, or technical issue, you can report feedback directly through the About & Contact section. Submissions include a 48-hour cooldown period to ensure orderly review by our team.