Medical Device Assembly Verification
Learn how to automate medical device assembly verification.
Add a real-time inspection layer to every stent with vision AI for stent inspection. Built for the inspection rooms where a device is a lattice of three hundred struts on a tube two millimeters wide, a crack shows itself for a few microns at one rotation angle, and every unit spends minutes under a microscope on its way to a bloodstream. Whether you're laser cutting tubes, electropolishing, or coating drug-eluting devices, Roboflow inspects every strut, crown, and coated surface, with per-device records behind every implant.
Struts and Cut Quality:
Coating and Surfaces:
Rooms, Lines, and Systems Integration:
Bring intelligence to every implant today. Stop strut and coating defects from becoming clinical events, field actions, or the inspection room bottleneck measured in minutes per device.
What is stent inspection with Vision AI?
Stent inspection with vision AI uses computer vision models to inspect stents through laser cutting, electropolishing, and coating: strut and crown cracks, nicks, and incomplete cuts, slag, dross, and burrs, electropolish pits, coating webbing, bare spots, and particulate on drug-eluting devices, and marker band verification. Models trained on your actual devices inspect every strut on every unit, with per-device records that support ISO 13485, FDA 21 CFR 820, and PMA documentation.
Can Vision AI inspect a stent's lattice?
The stent lattice stacks every hard case at once: hundreds of near-identical struts and crowns repeating around a polished tube a couple of millimeters wide, defects measured in microns, a mirror surface that throws glare, and geometry that has to be judged through rotation because a crack shows at one angle and hides at the rest. Deep-learning models trained on your actual designs, finishes, and rotational imagery learn each pattern's normal strut, edge, and coating, flag the crown that breaks it at any angle, hold that judgment across sizes and design families, and route uncertain devices to a scope instead of guessing, in seconds against the minutes a manual station takes.
Does this replace fatigue testing and SEM?
No. Fatigue, radial force, and expansion testing keep the functional record, run on sampled devices, and SEM keeps its qualification and failure-analysis role. Vision AI adds the every-device visual layer those methods can't give you: the nicked crown on a unit that was never going to be sampled, the coating web caught before crimping hides the surface, the slag flagged at the laser instead of the complaint file, on every device at production speed. Test data and visual findings land in the same per-device record, and a drifting trend in cut edges usually reaches the laser before the fatigue lab sees it.
Can it integrate with our inspection stations, PLCs, and MES?
Yes. Roboflow Inference exposes a standard API and supports common industrial protocols, so stent inspection events flow into your existing systems: inspection station and line PLCs from Allen-Bradley and Siemens, MES and ERP platforms like SAP and Oracle, and SCADA and HMI platforms like Ignition and AVEVA, through REST, MQTT, OPC UA, and direct database writes. Station-level integration routes a flagged device the moment a check fails, and every event carries device serial, lot, station, imagery, and disposition, with a full audit trail behind every implant.
