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Introducing Artificial Intelligence to pharmaceutical visual inspection

Inspection is a very challenging stage in the pharmaceutical manufacturing process. This is especially true for products with difficult characteristics, such as highly viscous parenteral solutions where air bubbles cannot be completely eliminated and their differentiation from particles is problematic. Those cases usually require long development and optimization times for vision algorithms before achieving a balanced operational level of detection and false reject rates.

The innovation for next level visual inspection

Artificial Intelligence has the potential of shortening this development period and optimizing the desired results more quickly. A classic win-win situation for both pharmaceutical manufacturers and patients, who receive high-quality and safe products.

“We believe that this technology has the potential to achieve detection rates close to 99 percent in the future while reducing false reject rates dramatically by half or more,” says Dr. Jose Zanardi, who is responsible for vision inspection development and applications at Bosch Packaging Technology. He is confident that the Deep Learning application can be implemented in a GMP environment – and will obtain regulatory endorsement for both the qualification strategy and implementation. This will significantly improve the inspection of products that are difficult to inspect, such as lyophilized products or those filled into complex primary packaging such as syringes or double-chamber systems. This will reduce reject rates and subsequently costs in the production of expensive products such as orphan drugs.

 

 

Our expertise builds on experience gained through case studies conducted with customers in the US and Japan. The results are very impressive indeed! Please feel free to contact us to receive further information. Artificial intelligence in visual inspection – far more than a vision!

 

Contact

Andreas Gross
Product Manager
E-mail: Andreas.Gross6@bosch.com
Phone.: +49 711 811 57461

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