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 ∼30 × 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 (1.7 × 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.