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AI Visual Inspection in Medical Device Manufacturing

The Medical Device industry is a rapidly evolving and increasingly competitive landscape. With a forecasted valuation of $800 billion by 2030, market participants are developing strategies to enhance their value to stakeholders and stay ahead of the curve. Many organisations are adapting new and advanced technologies into their manufacturing processes in a bid to negate the threat of new entrants and emerging markets. Artificial Intelligence (AI) is one of the key technologies being explored by medical device manufacturers to optimise quality control and efficiency.

Visual Inspection in Medical Device Manufacturing

Visual inspection is imperative in quality control and assurance processes of medical device manufacturing. Inspections are carried out to identify  defects, flaws, or irregularities that could affect the device’s functionality, safety, or usability. Although manual visual inspection is a common practice throughout the medical device industry, this method is subject to human error and inconsistencies. A flawed visual inspection of a medical device carries immense risk and can result in the device malfunctioning, leading to severe and even fatalistic consequences. Product recalls and lawsuits have cost companies millions of dollars, fallen share prices and reputational damage. Investing in quality control safeguards patient safety, ensures device functionality, and helps manufacturers comply with regulatory requirements, ultimately contributing to the overall effectiveness and reliability of medical devices.




Minimally Invasive Devices

Minimally invasive medical devices such as catheters, stents, balloon catheters, guidewires, biopsy needles are designed to perform procedures with minimal disruption to the patient’s body. Their manufacture and quality control requires meticulous attention to detail.

Taking a catheter device as an example, visual inspection requirements can vary based on factors such as the type of catheter, its intended use, the materials used, and applicable regulatory requirements. The below list however details some typical visual inspection criteria that manufacturers must assess their catheter device against:

  • Surface defects such as scratches, dents, cracks, and discoloration that could affect the device’s integrity or cause harm to the patient’s tissue.
  • Tip shape and integrity must be properly formed, smooth, and free from defects that could cause injury or discomfort during insertion.
  • Lumen must be free of any obstructions, blockages, or irregularities that could impede the flow of fluids or lead to complications during use.
  • Adhesive and bonding quality to prevent detachment during use.
  • Hub and connector for proper alignment, secure attachment, and absence of defects that could hinder proper connection to other devices.
  • Radiopaque markers correctly positioned and visible to aid in accurate placement.
  • Balloon integrity must be ensured and have an absence of defects that could affect its functionality.
  • Dimensional accuracy must be checked for any deviations from specified dimensions, including length, diameter, and curvature, to ensure proper fit and function.
  • Tapered transitions between different sections must be checked to ensure they are smooth and without irregularities that could cause tissue damage.

The impact of missing any of these defects can be significant, affecting patient safety, healthcare provider reputation, regulatory compliance, and financial stability. Therefore, medical device manufacturers must prioritise rigorous quality control measures, and employ advanced inspection technologies to avoid risk.

Artificial Intelligence Visual Inspection

AI-powered visual inspection is the most advanced technology capable of analysing medical device components with unparalleled precision. It consistently identifies even the most subtle defects or deviations from specifications. Moreover, it effortlessly generates comprehensive reports to ensure traceability and facilitate workflow enhancements, among other benefits.

Scratches, dents, cracks, misalignments, and other imperfections that are difficult to detect with the naked eye can be detected and visual data is analysed faster than the human eye, thus increasing the accuracy and throughput of quality control checks.

Incorporating AI visual inspection into medical device production will not only improve product quality but also enhance overall manufacturing efficiency, reduce cost, and most importantly, ensure patient safety. It is becoming increasingly evident that AI visual inspection has the potential to transform quality control processes in the medical device manufacturing industry.

Implementing AI Visual Inspection

Medical device manufacturers adopting AI visual inspection should take a systematic approach and consider partnering with AI technology providers or experts if internal expertise is limited. It is important to clearly define objectives for implementing AI visual inspection, such as improving accuracy, speeding up inspections, or reducing defects for the intended use case. Once a suitable AI technology has been selected, the responsibility is transferred to the process engineering departments to develop, configure and optimise the inspection models and process from inception to implementation.

A comprehensive dataset of images that represent the different types of defects with expected variations and lighting conditions that the AI system might encounter should be collected. This dataset can then be used to train AI inspection models using AI algorithms. Optimising an AI inspection model to achieve the best performance will be an iterative process. The AI system’s accuracy should be validated against known defect cases to establish its reliability. A gradual integration of the AI visual inspection system into the production process should be undertaken. AI-assisted visual inspection is a mode of operation that better facilitates the inspection operator to identify defects by leveraging the power of AI, while ultimately making the final decision. The performance of the system should be closely monitored during the pilot phase and the necessary adjustments made. Continuously monitor the AI system’s performance and gather feedback from the team and production lines, which should be used to fine-tune the AI models and improve the accuracy and efficiency of the inspection process.

By approaching the adoption of AI visual inspection systematically and with a commitment to quality, medical device manufacturers can enhance their quality control processes, reduce defects, and improve patient safety and overall operational efficiency.

Looking to kickstart your AI visual inspection journey? Talk to one of our experts today!


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