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

Jiao, Ziao and Zang, Zhenya and Wang, Quan and Chen, Yu and Xiao, Dong and Li, David Day Uei (2023) PAIM (πM) : portable AI-enhanced fluorescence microscope for real-time target detection. Optics and Laser Technology, 163. 109356. ISSN 0030-3992 (

[thumbnail of Jiao-etal-OLT-2023-PAIM-portable-AI-enhanced-fluorescence-microscope]
Text. Filename: Jiao_etal_OLT_2023_PAIM_portable_AI_enhanced_fluorescence_microscope.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (11MB)| Preview


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 grams with dimensions of 145mm × 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.