Artificial Intelligence for Microbial Colony Analysis: Plate-Reading Challenges, Method Selection, and Validation

Microbial colony analysis is routinely used in clinical microbiology, pharmaceutical quality control, food safety, and environmental monitoring. Colony enumeration remains challenging because real-world plates frequently show crowding, size variation, background debris, and plate-rim interference. Low contrast on membrane or filter media, weak colony boundaries, and ultra-dense or too-numerous-to-count (TNTC) growth further complicate analysis. These conditions increase counting variability and limit rule-based counters. This review examines artificial intelligence (AI)-enabled colony analysis and links recurrent plate-reading challenges to method selection and validation requirements. We examine traditional threshold- and morphology-based pipelines, object detection, semantic segmentation, instance segmentation, and density- or keypoint-based estimation. Current evidence is stronger for plate-level triage and semi-quantitative interpretation than for direct colony enumeration. However, evidence supporting routine quantitative colony-forming unit (CFU) reporting across laboratories remains limited. Reliable deployment requires methods suited to plate format, organism, imaging conditions, and intended use, with validation in the target workflow.