Machine Learning-Based Real-Time Localization and Automatic Trapping of Multiple Microrobots in Optical Tweezer

Ren, Yunxiao; Keshavarz, Meysam; Anastasova, Salzitsa; Hatami, Ghazal; Lo, Benny; Zhang, Dandan

doi:10.1109/marss55884.2022.9870467

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…[ 191 ] More Advanced Trapping techniques have emerged, which use machine learning technologies to automatically localize and trap microrobots in the OT. [ 192 ] Applications such as the selection and isolation of single cells for clonal expansion and RNA‐sequencing, selection and targeting of cell–cell fusion partners, and collection of precious microtissue specimens from complex samples [ 136 ] are also being explored.…”

Section: Optical Actuatorsmentioning

confidence: 99%

Actuation of Mobile Microbots: A Review

Hussein,

Damdam,

Ren

et al. 2023

Advanced Intelligent Systems

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Maturation of robotics research and advances in the miniaturization of machines have contributed to the development of microbots and enabled new technological possibilities and applications. Microbots have a wide range of applications, including the navigation of confined spaces, environmental monitoring, micro‐assembly and manipulation of small objects, and in vivo micro‐surgeries and drug delivery. Actuators are among the most critical components that define the performance of robots. A comprehensive review of the actuation mechanisms that have been employed in mobile microbots is provided, including piezoelectric, magnetic, electrostatic, thermal, acoustic, biological, chemical, and optical actuation, with a focus on the most recent development and methodologies.

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Section: Optical Actuatorsmentioning

confidence: 99%

Actuation of Mobile Microbots: A Review

Hussein,

Damdam,

Ren

et al. 2023

Advanced Intelligent Systems

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“…Recent initiatives aim to automate these systems, reducing human-induced Pedro Jorge: E-mail: pedro.jorge@fc.up.pt variability and significantly increasing both efficiency and consistency through automated optical and mechanical processes or signal analysis. [10][11][12][13][14][15] This paper evaluates the benefits of automation in the context of improving the system capabilities and reliability. It introduces an advanced optical tweezer system that is fully automated and features image recognition technology.…”

Section: Introductionmentioning

confidence: 99%

Automation of optical tweezers: an enabler for single cell analysis and diagnostic

Jorge,

Teixeira,

Rocha

et al. 2024

Biophotonics in Point-of-Care III

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Sensing at the single cell level can provide insights into its dynamics and heterogeneity, yielding information otherwise unattainable with traditional biological methods where average population behavior is observed. In this context, optical tweezers provide the ability to select, separate, manipulate and identify single cells or other types of microparticles, potentially enabling single cell diagnostics. Forward or backscatter analysis of the light interacting with the trapped cells can provide valuable insights on the cell optical, geometrical and mechanical properties. In particular, the combination of tweezers systems with advanced machine learning algorithms can enable single cell identification capabilities. However, typical processing pipelines require a training stage which often struggles when trying to generalize to new sets of data. In this context, fully automated tweezers system can provide mechanisms to obtain much larger datasets with minimum effort form the users, while eliminating procedural variability. In this work, a pipeline for full automation of optical tweezers systems is discussed. A performance comparison between manually operated and fully automated tweezers systems is presented, clearly showing advantages of the latter. A case study demonstrating the ability of the system to discriminate molecular binding events on microparticles is presented.

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“…The use of advanced signal processing schemes, using machine learning and artificial intelligence, in the extensive context of photonic systems, is rapidly expanding, where these methods are enabling a more efficient extraction of information from the optical signals. Consequently, more powerful sensing systems can be implemented, for instance, resulting in so-called, intelligent systems [4,[8][9][10][11][12][13]. While such approaches have a very strong potential, their reliability is very much dependent on the quality and dimension of the training sets, and usually drops considerably when facing 'real life' samples.…”

Section: Introductionmentioning

confidence: 99%

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Teixeira,

Moreira,

Oliveira

et al. 2023

Meas. Sci. Technol.

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Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

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Machine Learning-Based Real-Time Localization and Automatic Trapping of Multiple Microrobots in Optical Tweezer

Cited by 7 publications

References 26 publications

Actuation of Mobile Microbots: A Review

Actuation of Mobile Microbots: A Review

Automation of optical tweezers: an enabler for single cell analysis and diagnostic

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

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