Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets

Pulgar-Sánchez, Mary; Chamorro, Kevin; Fors, Martha; Mora, Francisco; Ramírez, Hégira; Fernández-Moreira, Esteban; Ballaz, Santiago

doi:10.1016/j.compbiomed.2021.104738

Cited by 16 publications

(6 citation statements)

References 56 publications

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“…30 What is more, this method adopted the non-radiative and noninvasive automatic diagnosis image-based method for the A-line and pleural line detection to obtain the accurate characteristics of lung status. Compared with these inspection methods, including chest CT, 38,39 chest X-ray, 40 and blood test, 41 the proposed method provided a novel idea that was more convenient, fast, and suitable for clinical application.…”

Section: Discussionmentioning

confidence: 99%

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Xing

et al. 2022

Medical Physics

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Background Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A‐line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A‐line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A‐line in LUS image is important. Purpose In order to solve the disadvantages of traditional A‐line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A‐line detection method for LUS images with different probe types in this paper. Methods First, the improved Faster R‐CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image‐processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A‐line detection. Finally, the “depth” index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five‐fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image‐processing‐based A‐line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. Results After 100 epochs, the mean loss value of training and validation set of improved Faster R‐CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R‐CNN model. The accuracy, sensitivity, and specificity of A‐line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians’ results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two‐stage experiment (pleural line localization and A‐line detection) was calculated as the final accuracy of the whole A‐line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. Conclusions The proposed method combining image processing and deep learning can automatically and accurately detect A‐line in LUS ima...

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Section: Discussionmentioning

confidence: 99%

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Xing

et al. 2022

Medical Physics

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“…Based on the above, Klann et al proposed an AI prediction model for COVID-19, using PaCO 2 as one of the predictors of patient prognosis [135] . Likewise, Pulgar-Sánchez et al hinted that PaCO 2 had a greater predictive relevance for severe COVID-19 pneumonia [136] .…”

Section: Experimental Design and Analysismentioning

confidence: 99%

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Zhang

Chen

et al. 2023

Biomedical Signal Processing and Control

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“…16,21,22 According to studies, these methods are effective and well-known tools for developing predictive and data analysis models and extracting useful information from the available data set 23,24 applied the Support Vector Machine (SVM) technique to demographic, clinical, and laboratory data of patients with COVID-19 to predict their ICU admission, mortality rate, and length of hospital stay. Also, 25 used laboratory data sets to predict the intensity of COVID-19 using data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Discovering associations between radiological features and COVID‐19 patients' deterioration

Ahmadinejad

Ayyoubzadeh

Zeinalkhani

et al. 2023

Health Science Reports

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Background and AimsData mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images.MethodsThis retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.ResultsTen rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.ConclusionThis study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

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Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets

Cited by 16 publications

References 56 publications

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Automatic detection of A‐line in lung ultrasound images using deep learning and image processing

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Discovering associations between radiological features and COVID‐19 patients' deterioration

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