Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review

Li, Yunxin; Zhang, Jinghui; Chen, Jisen; Zhu, Feng; Liu, Zhiqiang; Bao, Peng; Shen, Wei; Tang, Sheng

doi:10.1016/j.cclet.2023.109220

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…Thus, SDIC's versatility and educational applications could be expanded with digital videoing, where recording all phases of a colorimetric reaction will allow for holistic kinetic measurement and estimating reaction rates, constants, and other kinetic parameters. Additionally, SDIC's future will most likely include the use of machine learning and artificial intelligence for faster and more accurate colorimetric measurement or data analysis [76], thereby opening up new opportunities for a variety of educational applications and optimizations of the technique or specialized software that may reach even resource-constraint regions that would typically not reap the dividend of technological advancement.…”

Section: Future Perspectivesmentioning

confidence: 99%

Smartphone Digital Image Colorimetry: An Affordable Easy-To-Use Approach for Hands-On Training in Resource-Limited Settings

Markus,

Paul,

Marks

et al. 2024

Preprint

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Analytical instruments used for scientific research and education are often expensive. This limitation poses a challenge for schools and educators in low-income countries to provide practical science training to students. To address this problem, the improvisation of analytical instruments and instructional materials has emerged as a viable solution. By incorporating practical experiments into science education, students have the opportunity to engage directly with scientific instruments, conduct investigations, and collect data. This hands-on approach fosters a deeper understanding of scientific principles, enhances critical thinking skills, and cultivates scientific mindsets among students. Herein, we review and discuss, step-by-step, the approach of using a smartphone for quantitative analysis, referred to as smartphone digital image colorimetry (SDIC). SDIC, with its practicality and accessibility, utilizes smartphone digital cameras for image acquisition and the freely available image processing programs, such as ImageJ and smartphone Apps, for image quantification. Using SDIC can enable students in low-income countries to develop essential scientific skills, including critical thinking, data interpretation, and an evidence-based approach to scientific research. By leveraging the ubiquity and affordability of smartphone technology, SDIC offers an accessible and cost-effective approach to practical science education, bridging the gap between theory and practice in resource-constrained settings.

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Section: Future Perspectivesmentioning

confidence: 99%

Smartphone Digital Image Colorimetry: An Affordable Easy-To-Use Approach for Hands-On Training in Resource-Limited Settings

Markus,

Paul,

Marks

et al. 2024

Preprint

View full text Add to dashboard Cite

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Enhancing efficiency in detection of COVID-19 through AI-driven colorimetric isothermal detection with multiplex primers

Jaroenram,

Teerapittayanon,

Suvannakad

et al. 2024

Diagnostic Microbiology and Infectious Disease

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Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging

Chen,

Zhao,

Shi

et al. 2024

ACS Sens.

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Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument − irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.

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Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review

Cited by 3 publications

References 75 publications

Smartphone Digital Image Colorimetry: An Affordable Easy-To-Use Approach for Hands-On Training in Resource-Limited Settings

Smartphone Digital Image Colorimetry: An Affordable Easy-To-Use Approach for Hands-On Training in Resource-Limited Settings

Enhancing efficiency in detection of COVID-19 through AI-driven colorimetric isothermal detection with multiplex primers

Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging

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