In such a strictly regulated sphere as biopharma, a single microscopic clothing fiber or tiny glass cracks is not just a defect that leads to batch loss, but a multi-million dollar liability. As patient safety is at stake, the human eye can’t be the gold standard of quality control.
In many cases, even ML (traditional algorithms) can’t address the issue, and biopharma businesses need more sophisticated techs such as computer vision and deep learning for anomaly detection, i.e. cognitive monitoring.
In 2024, the machine vision market was valued at $14.81 billion, and this number is expected to reach $22.59 billion by 2032, growing at a compound annual growth rate of 8.7%.
What visual anomaly detection in biopharma is, how it works, why its popularity is growing, what benefits this tech brings, and how visual monitoring can be implemented in your particular case — read on to find the answers.
What is real-time anomaly detection in biopharma?
Anomaly detection in the pharmaceutical sector is the process where automated systems identify unusual behavior in biological processes (such as cell growth or protein production) in real time, before the batch is finished. As opposed to traditional methods, where you should wait for lab results for several days to detect failures, real-time visual inspection spots a tiny problem instantly.
Here’s how the whole process looks like:
- Data acquisition. The system collects a constant stream of data from physical IoT sensors (temperature, pH, dissolved oxygen, pressure) and specialized cameras (high-resolution video and images).
- Golden batch creation. Before the process of anomaly detection starts, the golden standard should be established. This is done through pulling historical data from hundreds of successful batches into a ML model and creating a multidimensional Digital Twin.
- Analysis. The incoming real-time data is compared against the baseline model. All the vital aspects are taken into account, for example, temperature can be normal, but the oxygen consumption is decreasing faster than expected. At this stage, any tiny anomaly that human operators can see is identified.
- Automated alerting. Once an anomaly has been detected, the system triggers an alert (notifying engineers to check the equipment), provides a root cause analysis (showing what exactly caused the deviation), and if needed automatically adjusts the nutrient feed to bring the batch back into the safe zone.
Real-time anomaly detection: Major benefits
As opposed to traditional sensor-based methods, anomaly detection algorithms can help your pharmaceutical business drive substantial value.
- Pharma quality control. Machine learning anomaly detection algorithms thoroughly inspect every single unit (vials, syringes, tablets) on the line and detect microscopic defects (tiny glass shards or cracks) that are invisible to the human eye. Contamination is also identified early on, preventing low-quality products from ever being finished.
- Slashed cost is reached through batch loss prevention. Namely, defective drugs and equipment are spotted in a matter of minutes, and companies eliminate the catastrophic legal and logistics costs associated with a global product recall.
- Optimized efficiency. Automated anomaly detection notably surpasses manual inspection in terms of quality and time. Computer vision systems operate at full machine speed, allowing the production line to run as fast as the hardware permits. Human specialists can save up to 80% of their time, investing it in more specialized and less-repetitive tasks.
- Faster time to market is guaranteed through faster environmental and cell tests (performed 24 hours earlier than manual incubation), instant real-time release testing (RTRT), eliminated inspection bottlenecks, and equipment predictive maintenance.
- Regulatory compliance. Automation around quality control allows for fast and strict quality compliance in the pharmaceutical industry. To wit, digital audit trail provides the unalterable evidence required during FDA and EMA audits. Also, AI-powered anomaly detection systems apply the same rules every time, ensuring the GMP (Good Manufacturing Practice) compliance of manufacturing facilities.
Key anomaly detection use cases
Underpinned by sensors, cameras, and advanced AI algorithms, anomaly detection systems are able to significantly improve your essential workflows. Here are some of the most popular examples of anomaly detection in biopharma.
Foreign matter detection
This is an automated process of spotting unwanted particles in a product, specifically vials, syringes, and ampoules. To avoid injecting such microscopic particles into a patient’s blood (thus causing life-threatening complications), an anomaly detection system usually leverages the Static Division Inspection (SDI) method:
- High-speed rotation. The vial is spun at high revolutions per minute so that the liquid and any potential particles could start moving. When the vial holder stops abruptly, the liquid continues to swirl due to inertia.
- Camera capture and anomaly identification. The camera captures a video and a burst of images and since the container is stationary, anything that moves in the video is identified as a potential foreign matter. These might be particles from the manufacturing process (glass shards, rubber fragments, stainless steel flakes), external contaminants (clothing fibers, dust, human hair), or undissolved active ingredients.
Cell culture monitoring
Automated anomaly detection can also be perfectly implemented in the sphere of cell monitoring. Cameras are placed directly inside or against the bioreactor to monitor cells while they’re growing. Usually, these key indicators are controlled to ensure quality compliance in the pharmaceutical industry:
- Cell morphology, namely, the size and shape of cells are monitored, as healthy cells have specific geometric profiles, and the dying ones shrivel or become irregular.
- Density. AI-based anomaly detection algorithms calculate the percentage of area covered or the number of cells per milliliter.
- Contamination includes detecting intruders such as bacteria or fungi that move differently than the production cells.
Then, the digital microscopy comes into play:
- Image capture. A professional high-resolution camera with a microscope lens takes photos of cells at particular intervals (for example, every 30 minutes).
- Segmentation. AI algorithms separate every individual cell from the background.
- Feature extraction presupposes simultaneously gauging thousands of cells for diameter, circularity, and granularity.
- Anomaly flagging. Finally, the AI-fueled anomaly detection model compares the current growth curve and cell shapes against a so-called golden batch to score anomalies.
Container integrity and seal inspection
Another anomaly detection example is about ensuring a completely sterile package for drugs. While sensors control the drug’s chemistry, visual monitoring presupposes detecting physical barriers:
- Vial and ampoule integrity. Computer vision systems identify micro-cracks (like hairline fractures in the glass) that are prone to stress during high-speed filling.
- Crimp and cap quality includes ensuring the aluminum seal is folded perfectly around the rubber stopper. The stopper position is also controlled, namely, the system detects the skewed ones that might let air or bacteria leak in.
- Syringe/plunger alignment is the automated checking of plungers to be seated correctly. Besides, the needle shield should be straight and intact.
Considering the fact that an anomaly might be hidden anywhere, a complex process is performed:
- Multi-camera sync. To analyze the containers while it moves, multiple (up to 6 items) high-speed cameras are installed. Thus, the vial is captured from every angle (top, bottom, sides).
- Specialized optics are used, such as polarized light or dark-field illumination. This makes an anomaly visible to the AI.
- Image stitching. An anomaly detection system turns the 3D vial into a 2D map, so that the AI could spot irregularities.
- Deviation mapping. The live container is compared against a digital twin of a perfect unit. Any irregularities like jagged lines, unexpected shadows, or surface breaks are detected.
Labeling and coding
A final gatekeeper, labeling, coding, and serialization is another anomaly detection machine learning example. In this case, visual monitoring is implemented to make sure the drug is accurately identified, legally compliant, properly labeled, and traceable before leaving the facility.
To wit, specialized cameras able to capture items moving at speeds of 400+ units per minute are installed to produce high-resolution images of the printed area. Then, the system leverages Optical Character Recognition (OCR) to read the text and convert pixels into data. Then, this data is compared against the “golden template” to analyze whether it matches exactly.
Here are some typical anomalies AI is able to detect:
- Physical labeling anomalies: misalignment and skew (when labels are off-center, tilted, etc.), surface defects (wrinkles, bubbles), damage (tears, folds, missing pieces), and presence errors (absence of a label, wrong label variant).
- Print quality anomalies: legibility issues (smudged, blurry, or faint text), content mismatch (omitted batch numbers, expiry dates, or regulatory symbols).
- Serialization and traceability anomalies that are critical for compliance with DSCSA or EU FMD: damaged 2D DataMatrix codes, duplication of serial codes, “parent-child” gaps, sequence errors.
Environmental monitoring
Visual inspection in pharmaceutical manufacturing means ensuring the “clean room” is sterile. In other words, it’s a high-volume inspection of air and surface samples.
- Automated colony counting includes using high-resolution camera and AI software to automate the process of identifying and counting Colony Forming Units (CFUs) on agar plates. Every individual bacterial or fungal spot (colony) is detected and marked with a digital tag. Pixel-level precision helps eliminate human errors.
- Early colony growth detection means noticing bacteria before they are visible to the human eye, slashing the time from 5 days to 24-48 hours. For that to happen, a camera is placed inside the incubator to take photos of the Petri dish every few hours. When tiny pixel changes are detected, it means a colony is starting to grow, helping humans to early detect contamination and fix the issue.
- Airborne particle tracking presupposes using specialized cameras and laser sensors in critical zones to notice and map the movement of dust, droplets, or microbes floating in the cleanroom air. Such visual monitoring helps understand the path of particles, i.e. from where exactly the dirt comes from, address the root cause, and instantly alert to stop work to prevent medicine contamination.
Implementation with Aetsoft
To make anomaly detection mechanisms work right, partner up with an expert with wide competence in artificial intelligence and computer vision. At Aetsoft, we’re ready to assist you at every stage of the implementation process:
- Comprehensive AI/ML consulting
- Full hardware set-up
- Dedicated AI model training, development, and customization
- Full-fledged AI visual inspection software development
You can also rely on us in ensuring utmost quality, rock-solid security, scalability, and full regulatory compliance. Connect with our team to discuss your future anomaly detection project in biopharma.
FAQ
How long does it take to implement visual inspection?
The timeline varies significantly and depends on several aspects, including your project’s complexity, the level of automation needed, whether there’s in-house computer vision competence, etc. In general, this multi-phase process might take from three to twelve months.
What are the challenges of implementing an anomaly detection model?
One of the most typical hurdles is data scarcity. So that your anomaly detection models would work without a hitch, train them on huge amounts of diverse datasets. An experienced machine learning development company will help you with this challenge.
Hardware and edge integration might become another issue, as processing high-resolution videos requires great computational power. In this regard, mind pairing up with an expert in IoT implementation.
How do I know whether my business needs visual inspection?
To understand whether it’s business- and cost-efficient for you to integrate computer vision in your biopharma workflows, analyze factors such as batch value (whether losing a single batch due to contamination or other anomalies costs you much), production bottlenecks (whether your packages lines could work faster), and false-reject rates (whether your current sensors throw away good medicine).
