Artificial Intelligence Non-Culturable Analysis
At Oceanic Laboratory Initiative (OLI), we embrace the power of Artificial Intelligence (Ai) to revolutionise mould analysis. The Ai analysis is validated and edited by qualified analysts before any report is issued, maximising the technological accuracy and reliability.
Unlike traditional methods, Ai does not conform to recognised standards for analysis, as these standards are tailored for human processes, manually operated equipment, and personal analyst capacities. Ai operates beyond these constraints, offering a more dynamic and efficient approach for multi-parameter analysis.
In AI-based mould analysis, achieving accurate results hinges on striking a delicate balance between two crucial metrics: precision and recall. These two metrics play a fundamental role in ensuring that the Ai model identifies and classifies mould structures with the highest level of accuracy.
Precision:
Precision in Ai analysis refers to the ability of the model to correctly identify mould structures when it detects them. In other words, it measures how many of the identified instances are truly relevant. A high precision score means that when the Ai identifies mould structures, it is highly likely to be accurate, minimizing false positives. This is especially critical in mould analysis, as false positives can lead to unnecessary concerns and actions.
Recall:
Recall, on the other hand, is the ability of the Ai model to find all the relevant mould structures in a given sample. It measures how well the model detects and captures all instances of mould, including those that might be less obvious or concealed. High recall ensures that no significant mould structures are missed during analysis, preventing false negatives where mould might be present but goes undetected.
Achieving a perfect balance between precision and recall can be challenging because they are often inversely related. Improving precision may lead to a decrease in recall and vice versa. Finding the right equilibrium is crucial, as it ensures that the Ai model accurately identifies mould while minimising both false positives and false negatives.
The effectiveness of precision and recall in Ai mould analysis depends on the quality of the data used to train the model and the hardware used to input the images. High-quality, well-labeled training data contribute to a model that can discern fungal structures from non-fungal elements with precision and can recognize mould structures across various scenarios with high recall.
At Oceanic Laboratory Initiative (OLI), the implemented Ai model is continuously learning and improving. By training on a vast dataset that includes diverse mould presentations, it refines its ability to balance precision and recall. As the model encounters new data, it can be manually reviewed and can be adapted and fine-tuned, striving for optimal accuracy in mould analysis.
The Ai model has been trained on an extensive dataset of over 200,000 sample images at the hands of a respected aerobiologist, with ongoing growth. This continuous learning process allows the model to consistently improve, enabling more comprehensive outputs as the database expands. This enhancement extends beyond mould identification and benefits other aspects of Indoor Air Quality analysis.
In summary, precision and recall are key metrics in Ai mould analysis, representing the model’s ability to accurately identify mould structures while minimising false positives and false negatives. Striking the right balance and continuously improving this balance is essential for delivering trustworthy mould analysis results.
Our commitment to quality includes a validation and editing process of the software outputs and is performed by qualified analysts. Before any report is issued, experts carefully review and fine-tune the results generated by Ai, ensuring the utmost precision and reliability of findings getting some of the best out of the Ai model and hardware for the final report.
It’s worth noting that Ai analysis, while powerful, does not adhere to conventional recognised standards for analysis like the ASTM D7391 – Standard Test Method for Categorisation and Quantification of Airborne Fungal Structures in an Inertial Impaction Sample by Optical Microscopy. This type of standard was developed for human processes, manually operated equipment, and personal analyst capacities, clarifying the standard operating procedure for this type of analysis. AI does not conform to these constraints, yet still offering a level of analysis that would be that has defensibility.
As this type of analysis relies on technology as a limitation, for more robust analysis it is advisable for critical projects to direct analysis through the ASTM analysis standard.
For air sample analysis, we currently support Apacor, Allergenco-D and Air-O-Cell cassettes. These cassettes are compatible with our AI-based analysis method, ensuring accurate results.
We believe in harnessing the potential of Ai to augment our analysis capabilities. Just like a child grows into a smarter adult, Ai learns and improves over time. Unlike regular programming, Ai adapts to new scenarios, making it more versatile and efficient.
Ai’s strength lies in its ability to process vast amounts of data, identifying patterns, making predictions, and recommending actions, much like a human but efficiently. At OLI, we see Ai analysis being most powerful for investigations and healthy home surveys.
At Oceanic Laboratory Initiative (OLI), we are committed to delivering reliable non-culturable mould analysis services that meet industry standards and exceed expectations.
We’re here to assist you every step of the way. Whether you have questions or need expert insights, our team is just a call or message away.
Mould Testing Analysis Servicing the Oceanic Region
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