Visual inspection is among the most important steps in supplying quality to pharmaceutical products during the manufacturing process. A visual inspection will help determine if every product is safe, pure and of good quality before being sent to the patients. This process will identify visible defects like particles, cracks, loose seals, discoloration or labeling issues that may cause quality issues with the product or pose a risk to the patient receiving them.
In the case of sterile manufacturing, even the smallest amount of contaminant can be dangerous to health; therefore, all regulatory agencies (FDA, EMA, WHO) stress 100% visual inspection of all injectable and sterile dosage forms. Historically, this job has been performed manually by trained operators using controlled lighting, but human inspection can be subject to error because of fatigue, subjective judgment and environmental variation.
Because of the desire for more consistent and efficient visual inspection processes, the pharmaceutical industry is increasingly utilizing Artificial Intelligence to transform the visual inspection from a manual, labor-intensive process into a reliable, data-driven operation.
- Particulate matter
- Incomplete seal
- Container breakage
- Incorrect fill volume
- Foreign matter (i.e., fibers)
- Discoloration of solution/powder
These defects may be introduced during manufacturing, packaging or handling of the product and if they are not detected can lead to product recall and/or regulatory agency warning and/or possible patient injury as a result of ingesting the defect into their body.
In a manual inspection workflow, the technician must inspect thousands of individual units during one shift (refreshed) and as a consequence of this “overload,” technicians become tired and their attention to detail diminishes. Therefore, some defects will go undetected and others will be incorrectly identified. As a result, the pharmaceutical industry is progressively moving towards automated inspection systems and AI-based inspection methods to allow for a more reliable and objective inspection outcome.
While rule based machine vision can successfully detect gross defects in a manufactured product, they have difficulty detecting small anomalies and intricate changes in products produced using a process. For instance, particles that are suspended in a fluid medium typically vary in both their physical characteristics and motion patterns. The same can be said for bubbles and scratches which are perceived as defects under specific conditions, but are not generally defects under others. As a result, there must be a more adaptable solution for detecting these types of defects and AI and neural networks are solutions of choice.
AI capabilities include:
1. Adaptive Learning: AI can adapt by "learning" from new data.
2. Precision: Artificial Intelligence( AI) can detect very small differences between accepted defects and typical variations.
3. Speed: AI has the ability to perform visual inspection at a speed of thousands of units in a minute without sacrificing accuracy.
4. Consistency: AI can provide visual inspection consistency regardless of human fatigue or subjective thought processes.
Example of how AI can be utilized is through visual inspections that include checking for micro-particles, cap misalignment, improper labels and tiny cracks to ensure that all products pass stringent quality specifications.
2. Continuous operation and speed of AI-enabled inspection allows you to eliminate any production bottlenecks.
3. AI always provides consistent quality regardless of when it is performed or what conditions exist at the time.
4. With an AI inspection system every inspection result is recorded and can be used for trending purposes as well as creating an audit trail for each inspection activity.
5. By enabling a scope of inspection that allows you to focus on making decisions and not on inspecting items repeatedly, AI inspection provides operators with fewer opportunities for making human error.
6. Documented records of inspection will enable your company to prove compliance with any local or national regulations during validation and audit processes.
7. By providing the benefits of automation AI inspection reduces the costs of rework, rejects and labor expenses associated with the use of manual inspection.
The first challenge is that AI requires a considerable amount of data (a very high volume) to train models.
Second, the regulatory agencies demand that any AI system be validated under a framework of Good Automated Manufacturing Practice (GAMP) or Good Manufacturing Practice (GMP).
Third, the integration of the AI into existing manufacturing execution systems (MES) and control software must be smooth. Fourth, some AI algorithms are very complex ("Black Box") and cannot be interpreted at the time of regulatory inspection due to their complexity.
The industry is working to find a solution by partnering with technology vendors and regulatory experts to build validation and transparency.
In order for pharmaceutical submissions to be approved, the visual inspection systems that have been developed using AI must demonstrate their reliability, validation and suitability for their intended purpose. To assist with proving this, documentation will need to accompany each submission providing sufficient details regarding training data, performance indicators and change control procedures used to maintain continuity of operations over time.
Federated learning will provide companies with a way to collaborate when building and training AI models while honouring the privacy of the data of individual companies. The continuing growth of AI will lead to a key role for AI in achieving zero-defect manufacturing and enhancing overall global quality.
Visual inspection is still a very important part of ensuring pharmaceutical quality assurance. Even though manual visual inspection has been around for many years, most pharmaceutical visual inspections are now being completed using AI technologies.
AI greatly increases the accuracy and consistency of visual inspection results, thus providing a means for drug manufacturers to provide greater levels of safety and compliance in their products than ever before. By adopting this technology, the pharmaceutical industry will increase their ability to assure product safety & product quality, thereby reducing recall rates while enhancing consumer confidence in the safety and efficacy of their products.
The use of AI will not replace humans performing visual inspections but will provide an enhanced way for humans to perform visual inspections. By doing so, visual inspection is being transformed from a reactive visual inspection process into a proactive, intelligent visual inspection function.
Because of the desire for more consistent and efficient visual inspection processes, the pharmaceutical industry is increasingly utilizing Artificial Intelligence to transform the visual inspection from a manual, labor-intensive process into a reliable, data-driven operation.
Importance of Visual Inspection in Pharmaceuticals
Visual inspection is your first line of defense in ensuring the quality of products and the safety of patients. Through visual inspection, you will be able to identify many types of defects including:- Particulate matter
- Incomplete seal
- Container breakage
- Incorrect fill volume
- Foreign matter (i.e., fibers)
- Discoloration of solution/powder
These defects may be introduced during manufacturing, packaging or handling of the product and if they are not detected can lead to product recall and/or regulatory agency warning and/or possible patient injury as a result of ingesting the defect into their body.
In a manual inspection workflow, the technician must inspect thousands of individual units during one shift (refreshed) and as a consequence of this “overload,” technicians become tired and their attention to detail diminishes. Therefore, some defects will go undetected and others will be incorrectly identified. As a result, the pharmaceutical industry is progressively moving towards automated inspection systems and AI-based inspection methods to allow for a more reliable and objective inspection outcome.
Transition from Manual to Automated Inspection
Automated inspection systems that employ digital graphics (high speed) and illumination systems (controlled) to create accurate reproductions of images of every product produced by a manufacturing process. The machines have traditionally been programmed using preset "rules"-if a specific measurement (such as light intensity or geometry) exceeded a predetermined threshold, that unit was considered defective.While rule based machine vision can successfully detect gross defects in a manufactured product, they have difficulty detecting small anomalies and intricate changes in products produced using a process. For instance, particles that are suspended in a fluid medium typically vary in both their physical characteristics and motion patterns. The same can be said for bubbles and scratches which are perceived as defects under specific conditions, but are not generally defects under others. As a result, there must be a more adaptable solution for detecting these types of defects and AI and neural networks are solutions of choice.
How AI Enhances Visual Inspection
Machine Learning and AI help inspection systems "learn" how to evaluate products based on their appearance using large datasets of images. AI models are trained on thousands of images representing good and bad products as opposed to relying on fixed thresholds. As these models iteratively receive more images, they develop the ability to identify characteristics, including patterns or anomalies, that might be difficult for a person or traditional inspection system to detect.AI capabilities include:
1. Adaptive Learning: AI can adapt by "learning" from new data.
2. Precision: Artificial Intelligence( AI) can detect very small differences between accepted defects and typical variations.
3. Speed: AI has the ability to perform visual inspection at a speed of thousands of units in a minute without sacrificing accuracy.
4. Consistency: AI can provide visual inspection consistency regardless of human fatigue or subjective thought processes.
Example of how AI can be utilized is through visual inspections that include checking for micro-particles, cap misalignment, improper labels and tiny cracks to ensure that all products pass stringent quality specifications.
Benefits of AI in Visual Inspection
1. AI gives you more accurate results in inspecting products by being able to find defects that were previously missed (false negatives) and not causing defects to be incorrectly identified (false positives).2. Continuous operation and speed of AI-enabled inspection allows you to eliminate any production bottlenecks.
3. AI always provides consistent quality regardless of when it is performed or what conditions exist at the time.
4. With an AI inspection system every inspection result is recorded and can be used for trending purposes as well as creating an audit trail for each inspection activity.
5. By enabling a scope of inspection that allows you to focus on making decisions and not on inspecting items repeatedly, AI inspection provides operators with fewer opportunities for making human error.
6. Documented records of inspection will enable your company to prove compliance with any local or national regulations during validation and audit processes.
7. By providing the benefits of automation AI inspection reduces the costs of rework, rejects and labor expenses associated with the use of manual inspection.
Challenges in AI Implementation
Implementing AI in pharmaceutical inspection has numerous challenges, though AI offers benefits.The first challenge is that AI requires a considerable amount of data (a very high volume) to train models.
Second, the regulatory agencies demand that any AI system be validated under a framework of Good Automated Manufacturing Practice (GAMP) or Good Manufacturing Practice (GMP).
Third, the integration of the AI into existing manufacturing execution systems (MES) and control software must be smooth. Fourth, some AI algorithms are very complex ("Black Box") and cannot be interpreted at the time of regulatory inspection due to their complexity.
The industry is working to find a solution by partnering with technology vendors and regulatory experts to build validation and transparency.
Regulatory Expectations
Maintaining patient safety and data integrity is a key priority of regulatory bodies; therefore, regulators want innovators to be able to innovate. The FDA's Process Analytical Technology (PAT) and Quality by Design (QbD) are examples of these initiatives that attempt to promote the incorporation of modern technologies, including artificial intelligence (AI), into the improvement of processes within manufacturing.In order for pharmaceutical submissions to be approved, the visual inspection systems that have been developed using AI must demonstrate their reliability, validation and suitability for their intended purpose. To assist with proving this, documentation will need to accompany each submission providing sufficient details regarding training data, performance indicators and change control procedures used to maintain continuity of operations over time.
Future of AI in Visual Inspection
AI has tremendous potential for application in the pharmaceutical sector. Future generations of these solutions will be able to identify defects in an object that cannot be seen by the human eye using 3D imaging, infrared scanning and hyperspectral imaging. AI will connect to smart manufacturing ecosystems where real-time release testing (RTRT) and predictive maintenance can be performed automatically based on the data collected during manufacturing.Federated learning will provide companies with a way to collaborate when building and training AI models while honouring the privacy of the data of individual companies. The continuing growth of AI will lead to a key role for AI in achieving zero-defect manufacturing and enhancing overall global quality.
Visual inspection is still a very important part of ensuring pharmaceutical quality assurance. Even though manual visual inspection has been around for many years, most pharmaceutical visual inspections are now being completed using AI technologies.
AI greatly increases the accuracy and consistency of visual inspection results, thus providing a means for drug manufacturers to provide greater levels of safety and compliance in their products than ever before. By adopting this technology, the pharmaceutical industry will increase their ability to assure product safety & product quality, thereby reducing recall rates while enhancing consumer confidence in the safety and efficacy of their products.
The use of AI will not replace humans performing visual inspections but will provide an enhanced way for humans to perform visual inspections. By doing so, visual inspection is being transformed from a reactive visual inspection process into a proactive, intelligent visual inspection function.
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