Computer Vision for Surgical Plate Identification
Use AI image recognition to identify orthopaedic surgical plates from X-ray images — searching a catalogue of 10,000+ plates with 98% accuracy.
The Problem
Orthopaedic surgeons regularly need to identify surgical plates and implants from X-ray images. When a patient returns for a follow-up procedure, the surgeon needs to know exactly which implant was used previously to select the right tools and approach.
In practice, patient records are often incomplete. The original surgery may have been at a different hospital, or documentation may list only a generic description rather than the specific product.
The fallback is manual identification — an experienced surgeon examines the X-ray and tries to match the plate against their mental catalogue. With thousands of plate designs from dozens of manufacturers, even the most experienced surgeons can only confidently identify a fraction.
Misidentification carries real risk: wrong removal tools, wasted operating theatre time, and delayed surgeries that extend patient pain and recovery.
The Solution
Arnold is a computer vision system that analyses X-ray images and identifies the specific product from a catalogue of over 10,000 plates, achieving 98% accuracy.
The system uses a deep learning model trained on thousands of labelled X-ray images, recognising distinguishing features: overall shape, screw hole arrangement, plate edge profiles, and subtle design details between manufacturers.
The workflow is straightforward. Upload an X-ray image, and the AI identifies the plate region, extracts visual features, and returns top matches ranked by confidence — with manufacturer, product name, catalogue number, and compatible instruments.
The system handles real-world messiness: partially obscured plates, suboptimal angles, and poor image quality. A novel "few-shot" learning approach achieved high accuracy even with limited training data per plate type.
The Outcome
Over 10,000 surgical plates identified since launch, dramatically reducing identification time from hours to seconds.
Surgeons could plan revision procedures with confidence. This reduced intraoperative surprises, shortened procedure times, and improved patient outcomes.
For implant manufacturers, the platform transformed their identification workflow. Sales representatives who spent hours per week on manual identification requests could handle them in minutes.
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