Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation

Mendels, David-A.; Dortet, Laurent; Emeraud, Cécile; Oueslati, Saoussen; Girlich, Delphine; Ronat, Jean-Baptiste; Bernabeu, Sandrine; Bahi, Silvestre; Atkinson, G; Naas, Thierry

doi:10.1073/pnas.2019893118

Cited by 46 publications

(44 citation statements)

References 11 publications

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“…Machine learning-based approaches had been widely utilized in understanding the epidemiology, clinical presentation, and outcomes of COVID-19, as well as its vaccine acceptance sentiments and vaccine side effects [49][50][51][52][53][54]. Artificial intelligence (AI) models could be as accurate as medical specialists in the diagnosis and prognosis of COVID-19; however, their current diagnostic accuracy needs to be improved through further integration of big datasets of radiographic and clinical information [49].…”

Section: Introductionmentioning

confidence: 99%

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Riad

Huang

Abdulqader³

et al. 2021

Vaccines

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Background: young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students. Methods: a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students’ attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students’ willingness to get the COVID-19 vaccine. Results: machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual’s trust of the pharmaceutical industry, the individual’s misconception of natural immunity, the individual’s belief of vaccines risk-benefit-ratio, and the individual’s attitudes toward novel vaccines. Conclusions: according to the socio-ecological theory, the country’s economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.

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Section: Introductionmentioning

confidence: 99%

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Riad

Huang

Abdulqader³

et al. 2021

Vaccines

View full text Add to dashboard Cite

show abstract

“…For this issue, AI could be an excellent solution. Recently, Mendels et al applied AI to improve test result interpretations of conventional LFIA assay for COVID-19 detection [ 133 ]. By developing their smartphone app, xRCovid, they largely removed human errors using machine learning.…”

Section: Role Of Smartphones In Disease Control and Surveillance During The Covid-19 Pandemicmentioning

confidence: 99%

“…Test results can be provided in 5–8 min with a high sensitivity of 97.4% and a specificity of 98.3%. Figure 10 shows a schematic diagram of a portable AI-aided smartphone LFIA system for COVID-19 detection based upon the above-mentioned research work [ 61 , 133 , 134 ].…”

Section: Role Of Smartphones In Disease Control and Surveillance During The Covid-19 Pandemicmentioning

confidence: 99%

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Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19

Hsiao

Pham

et al. 2021

Biosensors

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The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.

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“…10 AI based smartphone applications using machine learning can classify SARS-CoV-2 serological rapid diagnostic test results, reducing reading errors in comparison to reading by the naked eye thereby improving result interpretation. 11 AI in the field of cardiology during the pandemic has proven to be useful in providing advanced technologybased treatment and to predict the survival/mortality of a COVID-19 patient from heart failure. 12 By using algorithms, AI can predict and help treat complex heartrelated problems of COVID-19 patients, monitor information, raise timely alerts, repeatedly learn from the input dataset, assess congenital heart disease, angina and fibrillation issues.…”

Section: Aimentioning

confidence: 99%

A review of emerging innovations in COVID-19

Saggi¹

2021

Int J Community Med Public Health

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The COVID-19 pandemic has globally impacted humanity. Human health, productivity, social life and function is affected. Every country has felt effects, domestic as well as international. Emerging technologies also known as disruptive technologies have played a significant role in the pandemic. This literature review is a manifest overview on the utilization of existing technologies during the COVID-19 pandemic. The strengths, weakness, opportunities and threats of the innovative technologies under review have been summarized. Their benefits and further scope of disruptive innovations have been reviewed. The review aims to identify and highlight approaches/gaps for improvement and future application.

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Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation

Cited by 46 publications

References 11 publications

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19

A review of emerging innovations in COVID-19

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