Early detection of COVID-19 mortality risk using non-invasive clinical characteristics

Mahdavi, Mahdi; Choubdar, Hadi; Zabeh, Erfan; Rieder, Michael J.; Safavi‐Naeini, Safieddin; Khanlarzadeh, Vida; Jobbágy, Zsolt; Ghorbani, Amirata; Abedini, Atefeh; Kiani, Arda; Lashgari, Reza; Kamrani, Ehsan

doi:10.21203/rs.3.rs-86363/v1

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Not to mention the extended time period for the test completion contributes to ruling out RT-PCR as a reliable early detection and screening method [8][9][10]. In contrast to RT-PCR, diagnosis from other measurements such as chest Computed Tomography (CT) and blood factors is shown to be an effective early detection and screening method with high sensitivity in both detection [11] and anticipation of the severity of the disease [12].…”

Section: Introductionmentioning

confidence: 99%

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

et al. 2021

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The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona’s assistance.

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Section: Introductionmentioning

confidence: 99%

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

et al. 2021

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“…Thus, the neural network has proven effective for capturing nonlinearities present in quarterly time series data and for making accurate predictions. The machine learning methods were used in other studies (Gambhir et al, 2020;Majumder et al, 2021;Zoabi et al, 2021;Gavrilov et al, 2021;Kushwaha et al, 2020;Mahdavi et al, 2021). The methods such as ARIMA modeling, however, are capable of predicting with high accuracy the socio-economic consequences of pandemic only in a particular region.…”

Section: Literature Reviewmentioning

confidence: 99%

Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions

Наумов

Krasnykh

Otmakhova

2022

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Relevance. There is a perceived lack of methods that can accurately, reliably and comprehensively reflect the epidemiological situation in regions and its impact on their socio-economic development. The approaches that are currently described in research literature do not take into account the multivariance of scenarios of the COVID-19 pandemic, both in time and space. Research objective. The article aims to present a methodological framework that could be used to predict the socio-economic consequences of the COVID-19 pandemic in regions and to detect the most vulnerable regions. Data and methods. The study relies on a set of methods, including the methods of regression modeling, ARIMA forecasting and spatial correlation analysis. Results. The panel regression analysis has confirmed the negative impact of the pandemic on socio-economic development, in particular, the growth of overdue wage arrears, unemployment, arrears, the number of liquidated organizations, and the industrial production index. We have also identified the most vulnerable regions that need to be prioritized for government support. Conclusions. The resulting models and scenarios can be used by policy-makers to set the priorities of state policy for the economic support of the regions and stabilization of the epidemiological situation in the country.

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“…Not to mention the extended time period for the test completion contributes to ruling out RT-PCR as a reliable early detection and screening method [7–9]. In contrast to RT-PCR, diagnosis from other measurements such as chest Computed Tomography (CT) and blood factors is shown to be an effective early detection and screening method with high sensitivity in both detection [10] and anticipation of the severity of the disease [6].…”

Section: Introductionmentioning

confidence: 99%

ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

Yousefzadeh

Esfahanian

Movahed

et al. 2020

Preprint

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Background: With the global outbreak of COVID-19 epidemic since early 2020, there has been considerable attention on CT-based diagnosis as an effective and reliable method. Recently, the advent of deep learning in medical diagnosis has been well proven. Convolutional Neural Networks (CNN) can be used to detect the COVID-19 infection imaging features in a chest CT scan. We introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using the chest CT scans. Method: Our dataset comprises 2121 cases of axial spiral chest CT scans in three classes; COVID-19 abnormal, non COVID-19 abnormal, and normal, from which 1764 cases were used for training and 357 cases for validation. The training set was annotated using the reports of two experienced radiologists. The COVID-19 abnormal class validation set was annotated using the general consensus of a collective of criteria that indicate COVID-19 infection. Moreover, the validation sets for the non COVID-19 abnormal and the normal classes were annotated by a different experienced radiologist. ai-corona constitutes a CNN-based feature extractor conjoined with an average pooling and a fully-connected layer to classify a given chest CT scan into the three aforementioned classes. Results: We compare the diagnosis performance of ai-corona, radiologists, and model-assisted radiologists for six combinations of distinguishing between the three mentioned classes, including COVID-19 abnormal vs. others, COVID-19 abnormal vs. normal, COVID-19 abnormal vs. non COVID-19 abnormal, non COVID-19 abnormal vs. others, normal vs. others, and normal vs. abnormal. ai-corona achieves an AUC score of 0.989 (95% CI: 0.984, 0.994), 0.997 (95% CI: 0.995, 0.999), 0.986 (95% CI: 0.981, 0.991), 0.959 (95% CI: 0.944, 0.974), 0.978 (95% CI: 0.968, 0.988), and 0.961 (95% CI: 0.951, 0.971) in each combination, respectively. By employing Bayesian statistics to calculate the accuracies at a 95% confidence interval, ai-corona surpasses the radiologists in distinguishing between the COVID-19 abnormal class and the other two classes (especially the non COVID-19 abnormal class). Our results show that radiologists' diagnosis performance improves when incorporating ai-corona's prediction. In addition, we also show that RT-PCR's diagnosis has a much lower sensitivity compared to all the other methods.

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Early detection of COVID-19 mortality risk using non-invasive clinical characteristics

Cited by 4 publications

References 23 publications

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions

ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

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