Unsupervised Learning‐Assisted Acoustic‐Driven Nano‐Lens Holography for the Ultrasensitive and Amplification‐Free Detection of Viable Bacteria - Yang Zhou; Junpeng Zhao; Junping Wen; Ziyan Wu; Yongzhen Dong; Yiping Chen (Advanced Science)
Abstract Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and illness outbreaks. Here, an unsupervised learning–assisted and surface acoustic wave–interdigital transducer‐driven nano‐lens holography biosensing platform is developed for the ultrasensitive and amplification‐free detection of viable bacteria. The monitoring device integrated with the nano‐lens effect can achieve the holographic imaging of polystyrene microsphere probes in an ultra‐wide field of view (∽28.28 mm2), with a sensitivity limit of as low as 99 nm. A lightweight unsupervised learning hologram processing algorithm considerably reduces training time and computing hardware requirements, without requiring datasets with manual labels. By combining phage–mediated viable bacterial DNA extraction and enhanced CRISPR–Cas12a systems, this strategy successfully achieves the ultrasensitive detection of viable Salmonella in various real samples, demonstrating enhanced accuracy validated with the qPCR benchmark method. This approach has a low cost (∽$500) and is rapid (∽1 h) and highly sensitive (∽38 CFU mL−1), allowing for the amplification‐free detection of viable bacteria and emerging as a powerful tool for food safety inspection and clinical diagnosis.