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.
Meet our experts @ PDA Conference in South Korea!
Would you like to find out more about the Implementation of Deep Learning in automated inspection machines under production conditions? Then meet Jose Zanardi at the PDA Aseptic Manufacturing of Biopharmaceuticals Conference in Incheon, South Korea on October 1-2, 2019! He will show results of a Proof of Concept conducted with customers on a fully automated inspection machine running in production environment. The presentation will reveal insights on important factors for design and implementation of Depp Learning from practical view and highlight key points to enable pharmaceutical validation.
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