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5 October 2023 Deep learning for nanofluidic scattering microscopy
Henrik Klein Moberg, Bohdan Yeroshenko, Daniel Midtvedt, Joachim Fritzsche, Barbora Špacková, David Albinsson, Giovanni Volpe, Christoph Langhammer
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550J (2023) https://doi.org/10.1117/12.2676769
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
We show that a custom ResNet-inspired CNN architecture trained on simulated biomolecule trajectories surpasses the performance of standard algorithms in terms of tracking and determining the molecular weight and hydrodynamic radius of biomolecules in the low-kDa regime in optical microscopy. We show that high accuracy and precision is retained even below the 10-kDa regime, constituting approximately an order of magnitude improvement in limit of detection compared to current state-of-the-art, enabling analysis of hitherto elusive species of biomolecules such as cytokines (~5-25 kDa) important for cancer research and the protein hormone insulin (~5.6 kDa), potentially opening up entirely new avenues of biological research.
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Henrik Klein Moberg, Bohdan Yeroshenko, Daniel Midtvedt, Joachim Fritzsche, Barbora Špacková, David Albinsson, Giovanni Volpe, and Christoph Langhammer "Deep learning for nanofluidic scattering microscopy", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550J (5 October 2023); https://doi.org/10.1117/12.2676769
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KEYWORDS
Biomolecules
Optical microscopy
Deep learning
Scattering
Biological research
Computer simulations
Light scattering
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