RT Journal Article
SR Electronic
T1 Applying Machine Learning to the Visual Inspection of Filled Injectable Drug Products
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP pdajpst.2022.012796
DO 10.5731/pdajpst.2022.012796
A1 Romain Veillon
A1 John Shabushnig
A1 Lars Aabye-Hansen
A1 Matthieu Duvinage
A1 Christian Eckstein
A1 Zheng Li
A1 Andrea Sardella
A1 Manuel Soto
A1 Jorge Delgado Torres
A1 Brian Turnquist
YR 2023
UL http://journal.pda.org/content/early/2023/06/15/pdajpst.2022.012796.abstract
AB With machine learning (ML) we see the potential to better harness the intelligence and decision-making abilities of human inspectors performing manual visual inspection (MVI) and apply this to automated visual inspection (AVI) with the inherent improvements in throughput and consistency. This paper is intended to capture current experience with this new technology and provides points to consider (PtC) for successful application to AVI of injectable drug products. The technology is available today for such AVI applications. Machine vision companies have integrated ML as an additional visual inspection tool with minimal upgrades to existing hardware. Studies have demonstrated superior results in defect detection and reduction in false rejects, when compared with conventional inspection tools. ML implementation does not require modifications to current AVI qualification strategies. The utilization of this technology for AVI will accelerate recipe development using faster computers rather than by direct human configuration and coding of vision tools. By freezing the model developed with AI tools and subjecting it to current validation strategies, good assurance of reliable performance in the production environment can be achieved.