Particle generation and dispersion from high-speed dental drilling

We introduce the holographic air‐quality monitor (HAM) system, uniquely tailored for monitoring large particulate matter (PM) over 10 μm in diameter—particles critical for disease transmission and public health but overlooked by most commercial PM sensors. The HAM system utilizes a lensless digital inline holography (DIH) sensor combined with a deep learning model, enabling real‐time detection of PMs with greater than 97% true positive rate at less than 0.6% false positive rate and analysis of PMs by size and morphology at a sampling rate of 26 L/min for a wide range of particle concentrations up to 4000 particles/L. Such throughput not only significantly outperforms traditional imaging‐based sensors but also rivals some lower‐fidelity, nonimaging sensors. Additionally, the HAM system is equipped with additional sensors for smaller PMs and various air quality conditions, ensuring a comprehensive assessment of indoor air quality. The performance of the DIH sensor within the HAM system was evaluated through comparison with brightfield microscopy, showing high concordance in size and morphology measurements. The efficacy of the DIH sensor was also demonstrated in two 2‐h experiments under different environments simulating practical conditions, with one involving distinct PM‐generating events. These tests highlighted the HAM system’s advanced capability to differentiate PM events from background noise and its exceptional sensitivity to irregular, large‐sized PMs of low concentration.

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Generalizable deep learning approach for 3D particle imaging using holographic microscopy (HM)

Kumar M.,

Hong

2024

Opt. Express

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Despite its potential for label-free particle diagnostics, holographic microscopy is limited by specialized processing methods that struggle to generalize across diverse settings. We introduce a deep learning architecture leveraging human perception of longitudinal variation of diffracted patterns of particles, which enables highly generalizable analysis of 3D particle information with orders of magnitude improvement in processing speed. Trained with minimal synthetic and real holograms of simple particles, our method demonstrates exceptional performance across various challenging cases, including high particle concentrations, significant noise, and a wide range of particle sizes, complex shapes, and optical properties, exceeding the diversity of training datasets.

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Particle generation and dispersion from high-speed dental drilling

Cited by 3 publications

References 44 publications

Correlation between beverage consumption and droplet production during respiratory activity using interferometric Mie imaging experiment

Correlation between beverage consumption and droplet production during respiratory activity using interferometric Mie imaging experiment

Holographic Air‐Quality Monitor (HAM)

Generalizable deep learning approach for 3D particle imaging using holographic microscopy (HM)

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