The FDA classifies medical devices based on risk to patients, with Class I being the lowest and Class III the highest. Class II and Class III devices — such as infusion pumps, orthopedic implants, or cardiovascular catheters — carry greater patient risk, requiring more stringent design, testing, and postmarket controls.

Manufacturing has become faster, more precise, and increasingly automated – but that hasn’t made it immune to defects. In fact, the complexity of today’s production environments has introduced a new challenge: the tiniest deviation in process, machine behavior, or raw material consistency can snowball into full-blown quality failures. And by

In modern manufacturing, not all defects are created equal – but they are often treated as if they are. A minor blemish on a surface finish might be completely inconsequential to a product’s function or safety, yet it could still trigger batch rejections, customer complaints, or costly rework. On the

Discover how transparent quality protocols reduce friction between OEMs and contract manufacturers, ensuring efficiency, trust, and better product outcomes.

In medical device manufacturing, quality isn’t just a benchmark — it’s a life-critical standard. Medical devices, particularly surgical and implantable devices, demand near-zero defect tolerance. Even the smallest flaw can trigger downstream consequences: from failed procedures and hospital brand-switching to FDA oversight that can last months. Yet, despite stringent quality

In today’s competitive manufacturing landscape, the debate around manual vs. automated inspection is more relevant than ever. While many production lines have adopted inline inspection automation, manual quality control still plays a central role—though not without its limitations. Manual inspection, or manual quality control, is used by dozens of industries,

Introduction In the world of computer vision and object detection, precision is key. Whether it’s for quality control in manufacturing, autonomous driving, or medical imaging, detecting objects accurately is the first step in automating real-world processes. But sometimes, the detection system might produce redundant or overlapping bounding boxes for the

Medical devices must meet the highest quality standards to meet patient safety concerns. This means devices must be effective, defect-free, and minimize patient harm. For manufacturers, medical devices must also display consistency and conformity in performance and manufacturing while meeting current regulatory compliance. With AI-driven automated inspection tools, all of

Introduction Object detection has become a cornerstone in computer vision, enabling machines to not only identify what’s in an image but also where it is. From autonomous driving to manufacturing quality control, object detection is transforming industries. However, training robust object detection models can be a complex and time-consuming task.

With long-standing production processes in place, and lofty daily throughput requirements, finding the right time to rework and innovate a manufacturing workflow can be a challenge. However, this means that manufacturers often hold on to legacy processes for far too long — processes that actually throttle production capacity and generate

With the recent major advancements in AI and machine vision technologies, the appeal of using high-accuracy automated inspection systems to improve manufacturing quality control is quite clear. But, are the up-front implementation costs worth it? When evaluating whether or not AI inspection technology is right for your production process, it’s

Manufacturers of medical devices and critical components consistently struggle with quality control. An over-reliance on manual inspection — and the inherent human error that comes along with it — regularly leads to both financial and environmental waste, as well as potential harm to consumers and to the manufacturer’s brand reputation.

Introduction Image classification is a fundamental task in computer vision with applications in medical imaging, autonomous driving, security, and e-commerce. As deep learning models advance, evaluating them correctly ensures reliability, minimizes errors, and enhances real-world performance. Many data scientists rely on accuracy as the primary evaluation metric. However, accuracy can

The digital age thrives on visual content, from social media graphics to blockbuster movie visuals. Visual storytelling has become indispensable for brands, artists, and creators aiming to captivate their audiences. Enter artificial intelligence (AI), a game-changer in visual content creation. AI tools are enabling creators to produce high-quality visuals faster