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OnePetri: Accelerating Common Bacteriophage Petri Dish Assays with Computer Vision
Michael Shamash1, Corinne F Maurice1
1Department of Microbiology and Immunology, McGill University, Montreal, Canada.
PHAGE (New Rochelle, N.Y.)
|September 26, 2022
Summary
OnePetri is a new mobile app that rapidly counts bacteriophage plaques on Petri dishes using machine learning. It is significantly faster and more accurate than manual counting and other automated tools.
Area of Science:
- Microbiology
- Bioinformatics
Background:
- Bacteriophage plaque enumeration is crucial for various biological protocols.
- Manual counting is the current standard but is slow, error-prone, and limits throughput.
Purpose of the Study:
- To develop and validate OnePetri, an open-source mobile application for rapid and accurate bacteriophage plaque enumeration.
- To compare OnePetri's performance against manual counting and existing automated tools.
Main Methods:
- Developed OnePetri using a collection of trained machine learning models.
- Designed an open-source mobile application for user-friendly plaque counting on circular Petri dishes.
Main Results:
- OnePetri demonstrated a 30x speed increase compared to manual counting.
- Achieved lower relative error (13%) than Plaque Size Tool (86%) and CFU.AI (19%).
- Showed 1.7x faster detection times than Plaque Size Tool.
Conclusions:
- OnePetri offers a user-friendly, rapid, and precise solution for bacteriophage plaque enumeration.
- The application significantly improves upon existing methods in terms of speed and accuracy.
- OnePetri enhances throughput and reduces human error in phage research.
