Preventing Bi-Directional Flow in Material Locks with Video-Based AI Error Recognition

I often compare cross-contamination in biopharmaceutical manufacturing with forgetting to mute yourself on a Zoom call full of executives. Both slip-ups can lead to major headaches. Then, our contact persons in biopharmaceutical manufacturing wildly disagree with this comparison and yes, I get it: Cross-contamination can be much worse than exposing yourself on an unmuted zoom call. In the worst case it can lead to patient harm.

The Challenge: Human Error and Cross-Contamination

Maintaining unidirectional material flow is crucial to prevent cross-contamination in biomanufacturing. Bidirectional movements in material locks can introduce contaminants, which in turn are likely to compromise product safety. Despite rigorous Standard Operating Procedures (SOPs), human error caused by insufficient training, time constraints, or indifference (or a combination of the three) can lead to undetected cross-contamination, potentially resulting in severe consequences.

For instance, an operator might move materials back into a clean area after they've been exposed to a less controlled environment. Alternatively, contaminated material might be placed in or near the airlock while clean material is being transferred to the controlled area. Even though airlock doors typically have window panels allowing visibility, deviations due to human error can still occur.

The Solution: Video-Based AI Error Recognition

Video-based AI error recognition offers a cutting-edge solution to this challenge. Imagine a scenario where an operator is about to move contaminated materials towards the airlock while clean material in this airlock is still waiting to be fetched. This constitutes a breach of SOPs that prohibit bidirectional material flow.

By monitoring material locks and adjacent areas, video-based AI detects potential contamination risks and warns operators in real time that they are about to deviate from SOPs. Specifically, the AI solution detects human activities, while combining it with the information from within the material airlock that material is still waiting to be fetched. A camera placed at the door would recognize a person carrying materials and opening the door of the material lock. In addition, it would recognize the hand of this person moving towards the door handle and combine this recognition with the (also AI generated information) that there is still material waiting to be fetched in the material lock. The information generated by the AI system would go to the alarm which again would warn the the operator about his/her non SOP compliant behaviour in real-time before any cross-contamination can occur.

In practice, this means the operator is warned against taking contaminated material into a material lock and opening the door where a material flow in the opposite direction is not yet finished. Additionally, since material often stays in the lock for too long, an optical or audible signal could indicate to operators that material is still in the airlock waiting to be retrieved.

Invaluable Feedback and Hard Data for Inspections

Moreover, the data generated by the AI system provides relevant evidence for inspections and internal feedback. AI generated data can answer questions like: Has the time segregation of the material flow always been in place? How can operator training be improved? How can material lock constructions be enhanced to facilitate operator routines?

Enhanced Reliability with AI

AI-driven video monitoring is more reliable than human oversight alone. Operators often tend to interpret rules less strictly, might not be sufficiently trained, or could be in a hurry, not considering their non-compliant activities as threatening. Artificial intelligence monitoring, on the other hand, is incorruptible and strictly adheres to the rules. Customized solutions provide continuous, precise monitoring, detecting even subtle deviations that might be missed by human eyes. This constant vigilance ensures that the risk of contamination due to bidirectional movements is significantly reduced.

In other words: AI completely takes over one specific task of a human supervisor warns against potential non-compliant activities. With the difference, that while humans cannot always be everywhere and sometimes are tired or not vigilant enough, AI can do the job 24/7.

Conclusion

Integrating video-based AI error recognition in biopharmaceutical manufacturing enhances contamination control by preventing bidirectional movements in material locks. This technology not only improves safety and compliance but also boosts overall operational efficiency. Embracing AI innovations is essential for staying competitive and ensuring the highest standards of product quality.

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