Computer Vision in Pharmaceutical

Defective parts or products can lead to serious liability issues, shorter product life spans, safety concerns, recalls, and additional costs.

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Applications of AI for Pharma Manufacturing

Machine vision systems and AI help medical and pharmaceutical manufacturers with quality control via automated inspection. LandingLens offers a standardized machine learning platform that improves inspection accuracy and reduces development time in pharma manufacturing, allowing companies to easily scale projects to multiple facilities.

Pill Inspection

Prior to being placed into bottles and other containers, tablets, and capsules must be inspected for defects and abnormalities such as cross-contamination, broken pills, and incorrect pills. While machine vision systems have long helped with such visual inspection tasks, our deep learning software can improve the inspection accuracy of such systems. With LandingLens, you can set up an automated visual inspection, that can inspect large numbers of pharma products and devices, since it is trained on images of defective and non-defective material.

Vial Counting

Machine vision technology can automate the process of vial counting, saving time and removing the risks involved with human error, such as misplaced or missing vials and subsequent recalls. Machine vision systems have visual counting capabilities, but our deep learning AI software provides a secondary layer of protection in the pharmaceutical manufacturing industry to ensure the correct number of vials and that they do not have defects.

Medical Kits Inspection

The medical kit inspection can be challenging particularly in transparent packaging with its flexible variations of appearance.
LandingLens ensures your medical kits reach patients safely, with robust visual inspection methods including assembly verification and seal inspections for packages of all shapes, sizes, and levels of transparency.

Vial Contamination Inspection

Vials and syringes must be thoroughly inspected prior to being sent out, as contaminants in these containers can mean the difference between life and death. Vials and syringes must also be inspected for defects such as cracks to avoid potential safety problems, not to mention recalls, lost revenue, and damaged customer relationships. Machine vision technology can help with vial and syringe inspection, but rules-based algorithms might struggle with the various defect types and false positives caused by issues like transparency and reflections. LandingLens software allows teams to build reliable AI models, automating processes that were previously considered impossible to automate in the pharmaceutical industry.

LandingLens Benefits for Computer
Vision and AI in Pharmaceutical Manufacturing

LandingLens, an industry-first data-centric artificial intelligence (AI) visual inspection platform, helps improve inspection accuracy and reduce false positives. The end-to-end platform standardizes deep learning solutions that reduce development time and scale projects easily to multiple facilities across the globe. Discover the benefits of computer vision for pharma manufacturing and the use of AI in the pharmaceutical industry.

Meet Strict Regulations, Keep Compliant

Medical and pharmaceutical companies must follow strict regulations from agencies such as the Food and Drug Administration. Maintaining product quality is key to complying with these regulations. LandingLens deep learning software can help companies stay compliant by improving inspection

Manufacturers in this industry are also subject to process validations which help demonstrate that the manufacturing process will consistently produce conforming products. LandingLens can help support the validation process by maintaining clean, thorough data and results.

Achieve Zero Product Escapes

There’s no room for error when it comes to manufacturing environments, and this is especially true for medical and pharmaceutical manufacturing. Companies need machine vision and deep learning software systems to prevent defective products from leaving the factory floor.

This is crucial for avoiding corrective actions, increasing productivity, delivering quality products, and maintaining solid customer relationships.

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