COVID-19: accurate interpretation of diagnostic tests—a statistical point of view

Asai, Takashi

doi:10.1007/s00540-020-02875-8

Cited by 16 publications

(13 citation statements)

References 14 publications

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“…Negative pools were prepared with different samples to determine whether different negative-sample composition in the pool affected the detection of positive samples. Sensitivity and sensibility were determined as described [34][35][36].…”

Section: Sample Storage and Pre-treatmentmentioning

confidence: 99%

Population wide testing pooling strategy for SARS-CoV-2 detection using saliva

et al. 2022

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SARS-CoV-2 pandemic has forced frequent testing of populations. It is necessary to identify the most cost-effective strategies for the detection of COVID-19 outbreaks. Nasopharyngeal samples have been used for SARS-CoV-2 detection but require a healthcare professional to collect the sample and cause discomfort and pain to the individual. Saliva has been suggested as an appropriate fluid for the diagnosis of COVID-19. We have investigated the possibility of using pools of saliva samples to detect SARS-CoV-2 in symptomatic and asymptomatic patients. Two hundred and seventy-nine saliva samples were analyzed through RT-PCR of Envelope, Nucleocapsid and Open Reading Frame 1ab genes. Reproducibility assays showed an almost perfect agreement as well as high sensitivity (96.6%), specificity (96.8%), positive predicted value (96.6%), and negative predicted value (96.8%). The average Cycle Threshold of the genes detected was 29.7. No significant differences (p > 0.05) were detected when comparing the cycle threshold average of two consecutive reactions on the same positive saliva samples. Saliva samples have a higher median viral load (32.6) than in nasopharyngeal samples (28.9), although no significant differences were detected (p > 0.05). Saliva-pool samples allowed effective SARS-CoV-2 screening, with a higher sensibility (96.9%) on 10-sample pools than in 20-sample pools (87.5%). Regardless of pools size specificity was high (99.9%) and an almost perfect agreement was observed. Our strategy was successfully applied in population wide testing of more than 2000 individuals, showing that it is possible to use pooled saliva as diagnostic fluid for SARS-CoV-2 infection.

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Section: Sample Storage and Pre-treatmentmentioning

confidence: 99%

Population wide testing pooling strategy for SARS-CoV-2 detection using saliva

et al. 2022

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“…To enable appropriate inter-rater agreement, studies should focus on agreement rather than correlation indices [32][33]. In addition, future statistical analysis should consistently analyse the disease prevalence, for example, by using Negative Predictive Value and Positive Predictive Value parameters, as well as positive and negative likelihood ratios to reduce uncertainties regarding the validity of diagnostic tests [34], with the latter ratios only reported in one of the 12 studies [21]. In this way, meaningful analysis of the overall merit of AI-supported imaging-based COVID-19 detection beyond high-prevalence populations could be ensured.…”

Section: Legendmentioning

confidence: 99%

Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers

Kriza

Amenta

Zenié

et al. 2021

European Journal of Radiology

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“…The most common COVID-19 detection technique is real-time polymerase chain reaction (RT-PCR). It has a high percentage of false-negative findings and may take up to two days to receive results, while having a sensitivity range of 70 to 90 [ 9 ]; it may also produce a quite high number of false-negative effects and may take up to two days to obtain results. In some countries, it may take up to five days or more due to the overwhelming number of tests that need to be analyzed [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 Based on Chest X-rays Using Deep Learning

et al. 2022

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The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained.

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COVID-19: accurate interpretation of diagnostic tests—a statistical point of view

Cited by 16 publications

References 14 publications

Population wide testing pooling strategy for SARS-CoV-2 detection using saliva

Population wide testing pooling strategy for SARS-CoV-2 detection using saliva

Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers

Detection of COVID-19 Based on Chest X-rays Using Deep Learning

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