PAIM (πM): Portable AI-enhanced fluorescence microscope for real-time target detection

Abstract

We proposed a portable AI fluorescence microscope (πM) based on a webcam and the NVIDIA Jetson Nano (NJN), integrating edge computing techniques for real-time target detection. πM achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. Prepared microscopic samples and fluorescent polystyrene (PS) beads can be imaged clearly. πM’s body was fabricated by a 3D printer, weighing ∼ 250 g with dimensions of 145 mm × 172 mm × 144 mm (L × W × H), costing ∼$300. It has a similar brightfield imaging quality compared to benchtop microscopes (∼$13,000). The customized convolution neural network (CNN) inside the NJN can realize feature extraction, real-time PS bead counting, and red blood cell counting without data transfer and offline image processing. Compared with two model-free image processing methods (OpenCV and CLIJ2), our CNN method is robust in bead counting at different concentrations. Six aggregated beads can be correctly counted with 80 % accuracy. Regarding feature extraction and human RBC counting, our CNN also obtained closer results to the ground truth (GT) than the CLIJ2 method (GT: 201; CNN: 196; CLIJ2: 189). With a miniature size and real-time analysis, πM has potential in point-of-care testing, field microorganism detection, and clinical diagnosis in resource-limited areas.