Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia

Chabi, Marie‐Laure; Dana, Ophélie; Kennel, Titouan; Gence-Breney, Alexia; Salvator, Hélène; Ballester, Marie; Vasse, Marc; Brun, Anne; Mellot, François; Greniér, Philippe

doi:10.3390/diagnostics11050878

Cited by 8 publications

(9 citation statements)

References 35 publications

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“…A variety of quantitative techniques have been used to quantify COVID-19 infection in the early phase, ranging from simple densitometry to deep learning methods (95,96). Quantitative CT (QCT) assessment of severity of COVID-19 infection in the early phase of infection is an independent predictor of ICU admission and of mortality (97)(98)(99). QCT may also be used to assess sequential change in lung volumes and pulmonary opacity (99).…”

Section: Potential Role For Quantitative Imaging In Post-covid Lung Diseasementioning

confidence: 99%

CT of Post-Acute Lung Complications of COVID-19

et al. 2021

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The acute course of coronavirus disease 2019 (COVID-19) is variable and ranges from asymptomatic infection to fulminant respiratory failure. Patients recovering from COVID-19 can have persistent symptoms and computed tomography (CT) abnormalities of variable severity. At 3 months after acute infection, a subset of patients will have CT abnormalities that include ground glass abnormalities (GGO) and subpleural bands with concomitant pulmonary function abnormalities. At 6 months after acute infection, some patients have persistent CT changes to include the resolution of GGOs seen in the early recovery phase and the persistence or development of changes suggestive of fibrosis such as reticulation with or without parenchymal distortion. Predictors of post-COVID lung disease include need for intensive care unit (ICU) admission, mechanical ventilation, higher inflammatory markers, longer hospital stay and a diagnosis of acute respiratory distress syndrome (ARDS). Treatments of post-COVID lung disease are being investigated with anti-fibrotic agents being investigated for the prevention of post-COVID lung fibrosis. The etiology of post-COVID lung disease may be a sequela of prolonged mechanical ventilation, COVID-induced ARDS or direct injury from the virus. Future research is needed to determine the long-term persistence of post-COVID lung disease, its impact on patients and ways to prevent or treat it.

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Section: Potential Role For Quantitative Imaging In Post-covid Lung Diseasementioning

confidence: 99%

CT of Post-Acute Lung Complications of COVID-19

et al. 2021

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“…The pathological abnormalities of mycoplasma pneumonia in children can be seen on a CT scan, and the symptoms can help doctors diagnose patients more accurately 24 . It has been reported that Clinical worsening or mortality in patients with COVID-19 pneumonia can be predicted using a quantitative assessment of the radiological extent and severity of lung illness utilizing an autonomous 3D AI-based software 25 . However, the quantitative assessment of CT features of MPP by AI software has not been reported extensively.…”

Section: Discussionmentioning

confidence: 99%

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Qian,

Tao,

et al. 2023

Preprint

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Refractory Mycoplasma pneumoniae pneumonia（RMPP）prediction is a challenging but clinically significant challenge. A model based on AI-derived quantitative determination of lung lesions extent on initial computed tomography (CT) scan and clinical indicators has the potential to facilitate early RMPP prediction in hospitalized children. In this study, we conducted a retrospective cohort as a training set including 126 children with M. pneumoniae pneumonia (MP) admitted to Children’s Hospital of Nanjing Medical University, China from January 2019 to December 2020. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). Finally, a clinical-imaging prediction model was developed using Log2Φ and clinical characteristics. The area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Then we conducted a prospective study including 54 children with MP as a test set to validate the predictive model for RMPP from January to December 2021. The clinical model yielded the AUC of 0.810 and 0.782, and the imaging model yielded the AUC of 0.764 and 0.769 in the training set and the test set, respectively. The clinical-imaging model combining Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD) produced the highest AUC values of 0.897 and 0.895 in the training set and the test set, respectively. Our work demonstrated that using automated quantification of lung disease at CT combined with clinical data in MPP is useful to predict RMPP.

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“…To appraise COVID-19 infection in the initial phase, different techniques are used, including densitometry and deep learning methods [ 30 , 31 ]. Early phase quantitative CT assessment of severity is an autonomous forecaster of ICU hospitalization and mortality [ [32] , [33] , [34] ]. It could as well be used to appraise serial variations in lung volumes and pulmonary opacity [ 34 ].…”

Section: Discussionmentioning

confidence: 99%