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At Microtechnix, we are committed to advancing the field of microbiology quality control (QC) through innovative, reliable technologies. Our cutting-edge AI solutions are designed to enhance the accuracy, efficiency, and compliance of your QC processes.
One of the key features of our AI-powered systems is the use of locked state AI. This approach ensures that once the AI’s performance has been rigorously validated, it remains in a secure, unchangeable state, guaranteeing consistent results and full compliance with regulatory standards
Locked state AI means that once the AI model’s performance is verified and validated in real-world conditions, it is then securely “locked,” ensuring that its behavior and decision-making processes cannot be altered. This guarantees:
Traditional computer vision solutions comes with several challenges to efficiently automate image analysis, especially when shape and size of objects differ a lot.
Here’s why Vision AI can positively impact computer vision projects:
At Microtechnix, 2 vision AI template models are available for immediate deployment at customers:
These AI models can, on request, be re-trained with customer-specific data to further optimize performance characteristics to meet unique requirements. We provide a user-friendly environment for labeling images that can be used to re-train the existing models, enhancing their accuracy for specific tasks and environments. Once the model is deployed, it enters a locked-state. This means that the model’s parameters are fixed and cannot be altered without proper authorization, ensuring data integrity, security, and compliance with regulatory standards. This locked-state provides confidence that the AI’s behavior remains consistent and reliable throughout its usage, without unauthorized modifications.
Essential in the overall development of a performant vision AI model is the collection of high quality data, subsequent analysis and finally the validation of the data.
The dataset that supports the pre-trained models include balanced data buckets from :
This results in a highly diverse dataset representing a real world environment.
Data labelling was performed by in-house imaging and microbiology experts to ensure correct labels are applied across the entire dataset. By ensuring microbiology experts and imaging experts agree on the data label, the 4-eyes principle was applied to guarantee the highest quality labelled dataset.
The developed AI-models were trained on real-world representative datasets.
The key performance objectives are:
Datasets are split into 3 categories:
To ensure proper evaluation of the AI model performance. A balanced split of data buckets is made, enabling performance validation on large numbers.
Image analysis is a time intensive process and is prone to human errors. Assisted or automated diagnostics enables more accurate analysis at a fraction of the time and cost of traditional methods.
Implementing AI offers various benefits:
All benefits above can be realized keeping ALCOA+ principles in mind, ensuring full data integrity.
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Bioburden & sterility testing
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EMMA HT | High Throughput Microbiology QC
EMMA RL | Automate Microbiology QC
EMMA | Digitize Microbiology QC
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