Deep learning-based detection of bacterial swarm motion using a single image.

Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. Conventionally, the detection of bacterial swarming involves inoculating samples on an agar surface and observing colony expansion, which is qualitative, time-intensive, and requires additional testing to rule out other motility forms. A recent methodology that differentiates swarming and swimming motility in bacteria using circular confinement offers a rapid approach to detecting swarming. However, it still heavily depends on the observer's expertise, making the process labor-intensive, costly, slow, and susceptible to inevitable human bias. To address these limitations, we developed a deep learning-based swarming classifier that rapidly and autonomously predicts swarming probability using a single blurry image. Compared with traditional video-based, manually processed approaches, our method is particularly suited for high-throughput environments and provides objective, quantitative assessments of swarming probability. The swarming classifier demonstrated in our work was trained on