The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Majrashi, Naif A.

doi:10.3389/fmed.2022.1076184

Cited by 5 publications

(5 citation statements)

References 91 publications

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“…We did not compare different SARS-CoV-2 variants and thus this study cannot be generalized to different variants. There have been many studies demonstrating the value of utilizing machine/deep learning algorithms to detect SARS-CoV-2 infection using CXR scores [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 41 ]; it would be interesting to see whether CXR scores generated by machine/deep learning would produce similar results to those presented in this paper.…”

Section: Discussionmentioning

confidence: 72%

“…CT is, however, prone to cross-contamination and, thus, it is not widely used in the context of COVID-19 in the United States and elsewhere in the world, especially in the intensive care setting, due to the risk of cross-infection. By contrast, a portable chest X-ray (CXR) is convenient, readily available, can be brought to the patient’s bedside, and can be readily disinfected between uses [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Although a CXR has inferior diagnostic quality to CT, CXRs can be used to visualize characteristic ground-glass opacities and consolidation in the lungs associated with COVID-19 infection, helping with clinical diagnosis [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

et al. 2023

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Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

et al. 2023

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“…Residual pulmonary damage could also contribute to the development to new clinical disorders (such as cardiac disorders, hypertension, diabetes, and renal disorders) as well as the worsening of pre-existing clinical disorders among individuals with COVID-19 compared to those of matched controls [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], which has broad health implications. Thoracic imaging, such a computed tomography (CT), can be used to evaluate the residual pulmonary effects from COVID-19 and provide valuable insights into the long-term morphological changes in the respiratory system, with ground-glass opacities (GGO), consolidations, and fibrosis being characteristic features frequently identified in the chest imaging of individuals with acute COVID-19 [ 8 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Emerging evidence suggests that the above-mentioned lingering respiratory symptoms are often accompanied by distinct CT findings, providing a visual narrative of the protracted aftermath of SARS-CoV-2 infection.…”

Section: Introductionmentioning

confidence: 99%

Short- and Long-Term Chest-CT Findings after Recovery from COVID-19: A Systematic Review and Meta-Analysis

Babar,

Jamil,

Mehta

et al. 2024

Diagnostics

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While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1–6 months) and long-term (12–24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3–0.59]) and long-term (0.38 [0.23–0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (β = −0.137, p = 0.002), ground-glass opacities (β = −0.169, p < 0.001), increased for honeycombing (β = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.

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“…However, due to the importance of pulmonary manifestations 11 as well as the appearance of false positive and false negative results in RT‐PCR tests, the use of medical images has received more attention. 12 Also, 13 has shown that many research have used X‐ray and CT scan methods to diagnose and predict the severity of the disease of Covid‐19 to calculate the severity score of the disease based on the lung involvement.…”

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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The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Cited by 5 publications

References 91 publications

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Short- and Long-Term Chest-CT Findings after Recovery from COVID-19: A Systematic Review and Meta-Analysis

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

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