AI/ML 
Rozwój oprogramowania 
Application of AI/ML in recognizing bacterial strains based on Fresnel patterns
Client
A research and diagnostic organization was seeking an innovative approach to identifying bacterial strains in medical, food, and environmental applications. The goal was to reduce costs, shorten analysis time, and enable fast, automated detection of multiple bacterial species without the need for expensive and time-consuming laboratory procedures.
Challenge
Traditional bacterial identification methods had significant limitations:
- Results could take up to 7 days and required highly skilled personnel.
- Modern techniques like PCR were expensive and couldn’t detect all bacterial strains in a single sample.
The client needed an effective system to automate the identification process and significantly accelerate analyses.
Solution
The project was executed in three main phases:
- Design of the Imaging System and Initial Concept
A dedicated optical system was developed to capture Fresnel patterns of bacterial colonies — a key component enabling non-invasive morphological analysis. These image data formed the foundation for further analysis. - Data Processing and AI/ML Model Development
We created a pipeline for extracting numerical morphological and textural features, enabling the differentiation of bacterial strain patterns. An AI/ML model was then trained to recognize dozens of bacterial species or indicate no match when similarity was low. - Validation and Optimization
The model achieved high identification accuracy—over 96%—as confirmed by an independent UK-based laboratory. The system was designed for easy integration with routine microbiological procedures.
Results
- We developed a system that was enthusiastically received by the client’s lab teams. It reached an identification accuracy of over 96%, validated by a certified lab.
- Analysis time was reduced from 7 days to a maximum of 24 hours. The system can identify multiple bacterial strains in a single test.
- All hardware and reagents used in the solution comply with lab standards, with just one additional component. The same sample can also be analyzed using other methods, increasing diagnostic flexibility.
- The development process and collaboration with the client ran smoothly and were continuously monitored. Our team actively shared knowledge, making implementation and future use of the system easier.
Zastosowanie AI/ML w prognozowaniu ryzyka hipoglikemii u pacjentów z cukrzycą
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Jakub Orczyk
Członek zarządu / Dyrektor sprzedaży
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