AI Pharma: Defects Identification on the Production Line
The traditional manufacturing process of drugs seems risky today: it involves the possibility of human-made errors and it is not cost-effective in terms of time spent on the verification. Machine learning and computer vision for quality control are slowly becoming a reliable alternative to traditional operations.
Today pharma companies are looking for innovative solutions to adapt to the volatile market demand. Despite fast technological progress, pharma businesses still face a number of problems in drugs and batches manufacturing. For instance, in order to exclude human-made errors visual Inspection during manufacturing and packaging of batches should be performed.
This naturally increases the cost of labor and still leaves space for possible defects and flaws. Yet there is a solution to these typical issues in drugs and batches manufacturing and this solution is AI (Artificial Intelligence).
Artificial Intelligence is a generic term for a set of technologies. In case of the challenges described above, we will speak about two technologies: Machine Learning and Computer Vision. If we are talking about the most relevant and effective AI for pharma, then it is the most relevant choice.
Machine Learning for the pharma industry is the possibility to approximate, analyze, cluster, classify a huge amount of data and solve complex problems within a short period of time offering smart solutions.
Computer Vision in the pharma industry is the unique chance to categorize an incredible amount of images or videos for object detection and tracking. The object can be cracked on the pill or poorly printed word on the batch. Computer Vision will guarantee 100% accuracy and instant detection compared to visual detection performed by employees.
AI for Pharma Use Cases
Defects Identification in Packaging
AI technologies can be effectively used to enhance the production of drugs on the stage of packaging.
Machine Learning + Computer Vision for the defect identification on the production line and during packaging. Using these technologies it is possible to:
● decrease time consumption during the packaging stage up to 14% ● identify defects of packaging (texture, shape, size, bad packing, etc.) on the production line and exclude them from the production instantly ● significantly reduce labour cost and labour interaction
Technologies used for this use case:
CNN (convolutional neural network) approach for identification of sealing defects, medicine lackness in blisters, packaging defects, and batch defects The CV (computer vision) approach and customized CNNs may identify defects at any stage and require the development of the CNN that with adjustments (training and setting of hyperparameters ) may be used for most of the tasks. At some point, there might be a need to develop unique CNN for the particular task.
Tech Stack: TensorFlow, OpenCV, etc
Defects Identification in Medicine
Using Computer Vision and Machine learning it is possible to spot problems of drug production at the manufacturing line. Using AI technologies for medicine the customer was able to reach the following goals:
● identification of the medicine with defects (cracks, contamination, blotches, etc.) using CV and ML technologies ● reduction of the labour cost and labour interaction ● real-time detection of the flaws and instant notifications about the violations
Technologies Used: Convolutional Neural Network for the anomaly detection that will be developed and trained to satisfy the main goals of the task with the accuracy reaching 95+ %.
As the Tech Stack: TensorFlow, OpenCV, open-source and delivered to the customer site.
Artificial intelligence has incredible potential in the healthcare and pharma industries. The traditional manufacturing process seems risky today: it involves the possibility of human-made errors. More than that, it is not very cost-effective in terms of labor cost. Machine learning and computer vision for quality control are slowly becoming a reliable alternative to traditional operations.