Popular computer vision use cases in the pharmaceutical industry

AI is a revolutionary discovery in the field of technology across the globe. It provides a varied number of benefits that come with integrating AI technology into all existing technology. Computer vision, as a powerful field of AI, has the potential to make new innovations, upgrading and enhancing everything that it is applied to. AI vision application in pharmaceutical manufacturing has already begun with major use cases that mainly involve the replacement of manual inspection of packaging with automation. 

Computer vision applications in pharmaceuticals target generally improving and optimizing all processes involved in manufacturing processes. It significantly reduces any manual intervention in the production process. In this blog post, we will discuss the vital aspects of how Computer vision AI is becoming one of the largest emerging technologies that assist in digitizing medicine, life sciences and many more. AI in the Pharmaceutical Industry leverages AI with IoT for processing images of camera sensors for object detection and image recognition. 

How Computer vision is serving a key role in pharmaceuticals

Computer vision is capable of automating tasks that used to be done manually. Contrary to other technologies, camera solutions offer a minimal operational footprint impacting existing processes while administering a great amount of information. Automatic detection can result in early detection of defects in the production process, which in turn, leads to reduced cost and faster feedback. Computer vision systems obtain the images and video streams from cameras and analyze them utilizing AI software for detecting events, anomalies and deviations. 

Quality control & inspection of capsules

Real-time quality inspection of gelatin capsules is considered a relevant problem right from the point of industry productivity. The equipment problems and inconsistencies in the pharmaceutical manufacturing process help in detecting defects such as dents, size, colour, holes, double caps, dirt and bubbles. Automating quality inspection with computer vision offers an automated solution for addressing the problems. Thus, industrial image processing software analyzes digital video/image data generating actionable insights in real-time. 

Harnessing deep learning techniques for training and deploying machine learning models are relevant to deep learning software applications.  It is a system that helps in checking all machinery involved in manufacturing for any kind of defects and enables predictive maintenance of tools prior to any loss in production processes.

Automation of visual inspection      

Across several pharmaceutical processes, there remains some part of mandatory security checks involving the inspection of medicines. Implementing the automated visual inspection assists in detecting missing and broken glitches in blister packaging. Advanced detection depending on machine learning algorithms offers an automated and rapid method for drug production quality detection and control. For instance, the nonuniformity of blisters and changed colour of tablets are immediately detected with real-time detection.

Tracking employee movements 

Employee monitoring with Computer Vision in Pharma can be one of the most productive usages of AI. Within the workplace premises, real-time monitoring of staff ensures seamless productivity and increased concentration. It ensures employees are not loitering across the premises. With strict real-time surveillance, it becomes easier for authorities to track workforces.

Lot code & label verification with OCR & OCV

Pharmaceutical packaging comes with product information such as labels, lot codes and expiration dates. This data can be verified to ensure regulatory compliance and patient safety. Optical character recognition and optical character verification are the two facets of AI-enabled Healthcare Innovations with Computer Vision for verifying the printed data on each package accurately. It eradicates manual errors and prevents compliance issues.                 

Ensuring precision in vial inspection

The process of maintaining rigorous quality control standards while mass-producing vials of liquid medicine can be tedious. During manufacturing phases vials can be subjected to cross-contamination therefore, it is important to detect the issues in real-time helping pharmaceutical companies detect contaminants, recognize defects, diminish consumer risk, and improve product quality. 

Deep learning & edge learning

Deep learning algorithms can be seamlessly trained on large datasets for identifying intricate patterns allowing them to detect defects, contaminants, and several other anomalies with remarkable accuracy in pharmaceutical products. It provides real-time data analysis and decision-making improving overall efficiency. 

Automation & Robotics boost efficiency

AI-automated robotics play a significant role in streamlining pharmaceutical production processes. With the detection of defects and anomalies, AI robotic arms are equipped with precision grippers that can automatically eliminate the impacted products from the production line, diminishing human intervention and reducing the risk of errors. 

Final Thought

In traditional times, pharmaceutical factories used to be based on manual and semi-automatic quality inspection processes involving operators, training and experience. Several loopholes involved in manual operations can cause issues in subjectivity, consistency and restricted accuracy.  In recent years, with the development of computer vision technology, digital video and image processing has never been easier before. Deep learning has begun achieving human-level performance for different tasks. At Nextbrain, we enable pharmaceutical companies to build and operate AI Video analytics software applications on different next-gen software infrastructure. The high-end platform offers an integrated set of tools for unveiling the full application lifecycle. Right from image annotation and training of specific object detection models to securing deployment and privacy-preserving deep learning at the edge, vision AI models administer machine learning teams with full-scale control. Leveraging advanced visual programming with automated development features can bridge the gap between a business and its processes.