OnePetri: Accelerating Common Bacteriophage Petri Dish Assays with Computer Vision

Abstract

Introduction: Bacteriophage plaque enumeration is a critical step in a wide array of protocols. The current gold standard for plaque enumeration on Petri dishes is through manual counting. However, this approach is not only time-consuming and prone to human error but also limited to Petri dishes with countable number of plaques resulting in low throughput.

Materials and Methods: We present OnePetri, a collection of trained machine learning models and open-source mobile application for the rapid enumeration of bacteriophage plaques on circular Petri dishes.

Results: When compared against the current gold standard of manual counting, OnePetri was ~ 30x faster. Compared against other similar tools, OnePetri had lower relative error (~ 13%) than Plaque Size Tool (PST) (~ 86%) and CFU.AI (~ 19%), while also having significantly reduced detection times over PST (1.7x faster).

Conclusions: The OnePetri application is a user-friendly platform that can rapidly enumerate phage plaques on circular Petri dishes with high precision and recall.

Michael Shamash

PhD Candidate

My research focuses on phage-bacteria dynamics of the infant gut microbiota, during health and disease.