Artificial intelligence is moving into an area where pharmaceutical quality decisions depend heavily on images: identification and classification of subvisible particles in parenteral products.
A study published in the International Journal of Pharmaceutics in October 2026 evaluated convolutional neural network (CNN) models for classification of pharmaceutically relevant particles using microscopy images.
The study provides an important lesson for regulated AI that extends far beyond particle analysis:
A sophisticated model cannot compensate for an incomplete or unrepresentative training dataset.
Why Subvisible Particle Classification Is Difficult
Particle characterization in parenteral products can involve visually very different materials.
The researchers developed a pharmaceutical particle image library containing representative examples including:
- protein aggregates;
- silicone oil droplets;
- rubber particles;
- glass lamellae.
Glass lamellae were particularly important because different morphologies were deliberately represented in the training library.
Some particles are relatively distinctive.
Others are transparent, low contrast or visually similar to another particle type.
That makes automated classification considerably more difficult than recognizing highly differentiated objects in conventional computer-vision applications.
The Training Library Determined Model Robustness
The researchers compared CNN models trained with comprehensive particle libraries with models trained using more limited categories or incomplete morphological representation.
The result was clear.
Models trained on comprehensive and representative libraries showed high and stable classification performance across complex mixtures.
When important particle morphologies were missing from the training data, robustness decreased and misclassification increased.
The effect was particularly visible for transparent and low-contrast particles.
This is an important pharmaceutical AI principle:
Model performance is conditional on what the model has been allowed to learn.
A validation dataset may demonstrate excellent accuracy while still failing to reveal an important weakness if both training and validation datasets contain the same limited representation of the real process.
The GMP Question Is Therefore Bigger Than Accuracy
In a regulated application it is tempting to ask:
“What is the model accuracy?”
That is necessary, but insufficient.
For a pharmaceutical-quality application, additional questions should include:
- Which particle classes were represented during training?
- Were different morphologies within each class represented?
- Were difficult or ambiguous examples deliberately included?
- Are the data representative of routine manufacturing conditions?
- Are rare but quality-relevant particle types represented?
- Was performance challenged using realistic multi-particle mixtures?
- How does the model behave when it encounters something outside its training distribution?
This moves validation from a simple model-performance exercise toward a scientifically justified assessment of the entire data-model relationship.
Representative Data Become Part of the Control Strategy
For conventional computerized systems, requirements normally define what the software needs to do.
Machine-learning systems introduce another critical element:
the data used to establish the required behaviour.
For image-based pharmaceutical AI, the assurance chain could therefore look like:
Intended Use
↓
Particle Taxonomy
↓
Representative Image Library
↓
Data Curation and Provenance
↓
Model Training
↓
Independent Performance Testing
↓
Routine Performance Monitoring
A weakness at the image-library stage can propagate through every subsequent stage.
A Random Train-Test Split May Not Be Enough
This study also illustrates a broader AI-validation issue.
Randomly dividing a dataset into training and testing subsets can produce reassuring performance statistics.
But if both datasets originate from the same narrow image population, they may share the same blind spots.
For pharmaceutical applications, validation may therefore need deliberate challenge sets.
These could include:
- uncommon morphologies;
- low-contrast images;
- borderline classifications;
- complex particle mixtures;
- variation in imaging conditions;
- examples obtained independently from the original training population.
The objective should not simply be to prove that the model performs well on familiar data.
The more important question is:
Where does the model stop being reliable?
Explainability Was Also Investigated
The work did not rely only on classification accuracy.
The researchers also investigated feature importance and model behaviour using approaches including principal component analysis, decision trees and Gradient-weighted Class Activation Mapping (Grad-CAM).
Grad-CAM can show which regions of an image contribute most strongly to a CNN classification.
For pharmaceutical applications, this type of analysis can help determine whether a model is using scientifically plausible visual characteristics rather than irrelevant image artefacts.
Explainability should not, however, be confused with validation.
An attractive heat map does not prove that a model is fit for GMP use.
Its value is different:
it can provide additional scientific evidence that model behaviour is understandable and consistent with the intended analytical problem.
A Useful Lesson for AI Validation Beyond Microscopy
The same principle applies to many other pharmaceutical AI applications.
Consider models used for:
- visual inspection;
- environmental-monitoring trend analysis;
- deviation classification;
- predictive maintenance;
- spectroscopic analysis;
- process prediction.
In every case, model performance depends on whether the development data adequately represent the situations the model will encounter during actual use.
A model trained predominantly on normal operation may perform poorly when faced with the rare abnormal conditions that are most important to Quality.
Edge Cases May Be More Important Than Average Performance
For pharmaceutical Quality Assurance, average accuracy is often not the most relevant performance measure.
A model could classify 99% of routine images correctly while systematically misclassifying a rare but important defect.
From a patient-risk perspective, that could be less acceptable than a model with slightly lower overall accuracy but more reliable identification of critical abnormalities.
AI performance criteria should therefore be connected to:
quality risk and intended use
rather than selected only because they generate impressive statistical results.
Training Data Should Be Treated as a Controlled Asset
This also has implications for data governance.
For a GxP-relevant ML application, an image library should not simply be regarded as a collection of files used by the data-science team.
Depending on the application and risk, controls may need to address:
- data provenance;
- classification and annotation;
- expert review;
- version control;
- addition and removal of images;
- traceability to training runs;
- representativeness assessments;
- change control.
This is especially important when the model is retrained.
If the training dataset changes, the model changes - even when the software code remains identical.
A Better Definition of AI Validation
The study supports a broader interpretation of pharmaceutical AI validation.
Instead of:
Model + Accuracy Test = Validated AI
a more appropriate structure is:
Intended Use + Representative Data + Appropriate Model + Independent Challenge Testing + Explainability + Lifecycle Control
This closely aligns AI assurance with established pharmaceutical principles of scientific understanding and risk-based control.
Why This Matters for Pharmaceutical Companies
AI-based image analysis has significant potential in pharmaceutical Quality Control.
It can process much larger image populations than manual classification and may improve consistency and speed.
But the major lesson from this study is not that CNN technology is ready to replace pharmaceutical scientists.
It is that:
the quality of an AI decision cannot be separated from the quality and representativeness of the data used to develop the model.
For regulated pharmaceutical AI, the training dataset may therefore become as important to validation as the algorithm itself.
Regulatory Status Note
This is a peer-reviewed scientific publication and does not constitute FDA, EMA, pharmacopoeial or other regulatory guidance.
The study demonstrates technical and scientific approaches to machine-learning-based particle classification. It does not establish regulatory acceptance of CNN classification as a replacement for any required compendial or validated analytical procedure.
The GMP observations above represent an interpretation of the study’s findings in the context of pharmaceutical AI lifecycle control.
Sources
Mohan S. et al. - Developing machine learning models with pharmaceutically relevant microscopy images to improve classification of multi particulate systems for parenteral delivery. International Journal of Pharmaceutics. Volume 703, 5 October 2026, Article 127313.
https://pubmed.ncbi. … lm.nih.gov/42603574/
DOI: