Software

Vision AI

EMMA uses Vision AI to raise the efficiency and efficacy of your microbial QC. Welcome to the next level of automation in quality control!

What is vision AI?

Vision AI harnesses the power of artificial intelligence to analyze and interpret visual information, offering valuable insights for microbiologists studying diverse organisms and ecosystems. By leveraging advanced machine learning algorithms, vision AI can identify specific features within images, such as bacteria, color identification, or growth patterns. This technology enablesmicrobiologists to automate time-consuming repetitive tasks, such as image classification or object detection, streamlining data analysis and accelerating quality control processes. Additionally, vision AI facilitates the extraction of meaningful information from large datasets.

Why vision AI?

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:

‍Faster image labelling
Full traceability & transparency
The ability to resolve much complexer use cases
Scalable & Robust

At Microtechnix, 2 vision AI template models are available for immediate deployment at customers:

EMdi assistant: detects colony growth on plates based on a binary classification model
EMdi automation: automatically counts colonies on positive plates based on an object detection model

These AI models can, on request, be re-trained with customer specific data to further optimise performance characteristics to match specific customer requirements. Based on a secure cloud platform, we provide a user-friendly environment to label images that can be used to re-train the existing models to perform better for the specific tasks and environments its used for.

Building a representative dataset

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 :

Negative plates (no colony)
Plates containing defects (writing, crack, injection point, etc.)
Plates with fogging (light condensation)
Positive plates (bacteria present)
Positive plates (mold present)

This results in a highly diverse dataset representing a real world environment.

Data Labeling

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.

AI training

The developed AI-models were trained on in-house generated representative datasets.

The key performance objectives are:

No false negatives
<5% false positives
Counting accuracy exceeding 98% for Petri dishes with less than 100 CFUs
Able to run on a local computer (edge device)

Validation

Datasets are split into 3 categories:

Train
Validate
Test

To ensure proper evaluation of the AI model performance. A balanced split of data buckets is made, enabling performance validation on large numbers.

How can end users benefit from AI solutions?

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.
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Implementing AI offers various benefits: