Should IgM/IgG rapid test kit be used in the diagnosis of COVID-19?

Burog, Aldrich Ivan Lois D.; Yacapin, Clarence Pio Rey; Maglente, Renee Rose O.; Macalalad-Josue, Anna Angelica; Uy, Elenore Judy B.; Dans, Antonio L.; Dans, Leonila F.

doi:10.47895/amp.v54i0.1558

Cited by 8 publications

(10 citation statements)

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“…RAT testing is based on IgM/IgG antibodies. Low specificity (77.8%) and sensitivity (18.8%) have been the main drawbacks of this method [4]. Therefore, emphasis is being given to other methods of testing that might be more accessible and less expensive in the future.…”

Section: Introductionmentioning

confidence: 99%

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Chadaga

Chakraborty

Prabhu

et al. 2022

Interdiscip Sci Comput Life Sci

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Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients. Graphical abstract

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Section: Introductionmentioning

confidence: 99%

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Chadaga

Chakraborty

Prabhu

et al. 2022

Interdiscip Sci Comput Life Sci

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show abstract

“…Moreover, a shortage has always been observed of RT-PCR test kits worldwide. Just like RT-PCR, IgM/IgG antibodies tests have their own disadvantages with sensitivity and specificity being as low as 18.8% and 77.8% respectively in initial screening of COVID-19 [4]. Although CT scans and chest X-rays images based on Machine learning [5] have shown positive results, however these tests are not much useful due to high dosage of radiation.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection from CBC using Machine Learning Techniques

Akhtar¹,

Akhtar²,

Bakhtawar³

et al. 2021

Int. J. TIM

111

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Covid-19 pandemic has seriously affected the mankind with colossal loss of life around the world. There is a critical requirement for timely and reliable detection of Corona virus patients to give better and early treatment to prevent the spread of the infection. With that being said, current researches have revealed some critical benefits of utilizing complete blood count tests for early detection of COVID-19 positive individuals. In this research we employed different machine learning algorithms using full blood count for the prediction of COVID-19. These algorithms include: “K Nearest Neighbor, Radial Basis Function, Naive Bayes, kStar, PART, Random Forest, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Further, “Accuracy, Recall, Precision, and F-Measure” are the performance evaluation measures that are utilized in this study.

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“…In this case, antibodies are detected in the patient's blood samples, depending on factors such as age, nutrition, disease severity and medications. However, recent studies have shown that the immune response is very weak, late or even absent in many cases of patients confirmed with Covid-19 [12][13][14][15][16][17][18]. This means that this type of detection is often only possible in cases of recovered patients.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences

Gomes

Masood

Silva

et al. 2020

Preprint

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The proliferation of the SARS-CoV-2 virus to the whole world caused more than 250,000 deaths worldwide and over 4 million confirmed cases. The severity of Covid-19, the exponential rate at which the virus proliferates, and the rapid exhaustion of the public health resources are critical factors. The RT-PCR with virus DNA identification is still the benchmark Covid-19 diagnosis method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. This technique analyzes the DNA sequences obtained by the RT-PCR method, eliminating sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. Experiments with all 24 virus families and SARS-CoV-2 (multi-class scenario) resulted 0.822222 ± 0.05613 for sensitivity and 0.99974 ± 0.00001 for specificity using Random Forests with 100 trees and 30% overlap. When we compared SARS-CoV-2 with similar-symptoms virus families, we got 0.97059 ± 0.03387 for sensitivity, and 0.99187 ± 0.00046 for specificity with MLP classifier and 30% overlap. In the real test scenario, in which SARS-CoV-2 is compared to Coronaviridae and healthy human DNA sequences, we got 0.98824 ± 0.01198 for sensitivity and 0.99860 ± 0.00020 for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify SARS-CoV-2 DNA sequences faster with higher specificity and sensitivity.

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Should IgM/IgG rapid test kit be used in the diagnosis of COVID-19?

Cited by 8 publications

References 0 publications

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

COVID-19 Detection from CBC using Machine Learning Techniques

Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences

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