Exploring the Potential of a Multispectral-Sensing System With Automated Machine Learning for Multiplex Detection

Huerta, Juan M.; Pirbhai, Massooma

doi:10.1109/jsen.2023.3304195

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…To leverage the full potential of the spectral sensor, we utilized its ability to capture emissions across multiple detection channels for the three excitation sources and simultaneously employed all the data instead of relying on optimized sources and detection channels for a particular fluorophore. By utilizing ML, we identified patterns within the data, as multiple studies have demonstrated ML’s effectiveness in demixing combinatorial emissions in biological fluorescence detection and imaging. − Figure a depicts the implementation process of a supervised ML model initiated by collecting calibration data from 125 fluorophore mixtures. These mixtures were prepared at five specific concentrations (0.00, 0.25, 0.50, 0.75, and 1.00 μM) of FAM, ATTO550, and Cy5.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications

Kshirsagar,

Politza,

Guan

2024

ACS Sens.

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There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and compact multiplexable systems remains a substantial challenge. This difficulty largely stems from the inherent need for spectrum separation, which typically requires sophisticated and expensive optical components.Here, we demonstrate a compact, lens-free, and cost-effective fluorescence sensing setup that incorporates machine learning for scalable multiplexed fluorescence detection. This method utilizes low-cost optical components and a pretrained machine learning (ML) model to enable multiplexed fluorescence sensing without optical adjustments. Its multiplexing capability can be easily scaled up through updates to the machine learning model without altering the hardware. We demonstrate its real-world application in a probe-based multiplexed Loop-Mediated Isothermal Amplification (LAMP) assay designed to simultaneously detect three common respiratory viruses within a single reaction. The effectiveness of this approach highlights the system's potential for point-of-care applications that require cost-effective and scalable solutions. The machine learning-enabled multiplexed fluorescence sensing demonstrated in this work would pave the way for widespread adoption in diverse settings, from clinical laboratories to field diagnostics.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications

Kshirsagar,

Politza,

Guan

2024

ACS Sens.

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Signal Quality in Continuous Transcutaneous Bilirubinometry

Crivellaro,

Costa,

Vieira

2024

Sensors

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Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device’s placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas.

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Exploring the Potential of a Multispectral-Sensing System With Automated Machine Learning for Multiplex Detection

Cited by 2 publications

References 37 publications

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications

Signal Quality in Continuous Transcutaneous Bilirubinometry

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