Development of an AI-Powered Microorganism Detection and Classification System for Laboratory Automation

Manual counting and classification of microorganisms on Petri dishes is time-consuming, error-prone, and requires highly trained personnel, leading to inconsistencies and limited throughput in microbiology laboratories. Existing manual methods hinder scalability and rapid decision-making in sectors like healthcare, food safety, and pharmaceuticals.

A medium-sized biomedical research organization seeking to automate microbiological analysis to improve accuracy and efficiency.

• Develop a fully automated AI-powered system capable of detecting, counting, and classifying bacterial colonies on Petri dish images with high precision.
• Create a flexible, customizable software library that can be adapted for new microorganism types, imaging setups, and laboratory workflows.
• Ensure compatibility with various hardware configurations, including standalone analysis and integration with existing lab automation systems.
• Achieve robust performance across diverse image acquisition conditions, including different lighting and camera parameters.
• Reduce analysis time and human error, allowing labs to increase throughput and diagnostic accuracy.

• Deep neural network models for accurate detection and classification of multiple microorganism types.
• Image processing pipeline optimized for microbiological imagery, including segmentation and object detection.
• Domain adaptation techniques to ensure model robustness across different imaging conditions.
• A user-friendly interface for analyzing pre-collected images or real-time image acquisition setups.
• APIs and library components for seamless integration with laboratory automation systems.
• Configurable parameters to customize analysis based on laboratory requirements, such as camera settings and microorganism profiles.
• Data management capabilities, including storage of annotated images and analysis results in a relational database.

• Image acquisition devices including standard microscopes with mounted cameras or tripod-mounted imaging setups
• Laboratory automation systems for seamless data flow and process orchestration (e.g., microbiology analysis workflows)
• Existing data management platforms to import/export analysis results

• High accuracy in microorganism detection and classification (above 95% accuracy as target)
• Scalability to handle large datasets of images (thousands per day)
• Robust performance under varying imaging conditions with domain adaptation techniques
• Security and privacy compliance for handling laboratory data
• Ease of use with minimal manual intervention and fast analysis turnaround times

Implementation of this automated microbiological analysis system is expected to significantly increase processing speed, reduce human error, and improve classification accuracy. It will enable laboratories across healthcare, food safety, and pharmaceutical sectors to handle larger sample volumes efficiently, leading to faster diagnostics, improved data quality, and cost reductions. The adaptable library approach aims to future-proof laboratory workflows against emerging microorganism detection needs.