CRISPR (Clustered regularly interspaced short palindromic repeats)‐based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS‐CoV‐2 molecular detection at the point of need. However, efficient translation of CRISPR‐diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence‐based results readout. Herein, an amplification‐free CRISPR/Cas12a‐based diagnostic technology for SARS‐CoV‐2 RNA detection is presented using a smartphone camera for results readout. This method, termed C ellphone‐based a mplification‐free s ystem with C RISPR/C A S‐ d ependent e nzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase‐generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS‐CoV‐2 reverse‐transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL −1 ) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 – 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR‐positive COVID‐19 patients.

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphonetaken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.