Improving diagnostic accuracy in assessing pulmonary edema on bedside chest radiographs using a standardized scoring approach

Hammon, Matthias; Dankerl, Peter; Voit-Höhne, Heinz Leonhard; Sandmair, Martin; Kammerer, Ferdinand J.; Uder, Michael; Janka, Rolf

doi:10.1186/1471-2253-14-94

Cited by 24 publications

(17 citation statements)

References 40 publications

(51 reference statements)

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“…Automated evaluation of pulmonary edema severity on CXRs has been explored using a deep learning model that incorporates ordinal regression of edema severity labels in training (no, mild, moderate, or severe edema) ( 24 ). These severity labels were extracted from associated radiology reports, but are inherently noisy given the variability in interpretation of the CXRs ( 25 , 26 ). This problem of noisy labels extends beyond pulmonary edema to any disease process where there is subjectivity in interpretation.…”

Section: Discussionmentioning

confidence: 99%

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

Li¹,

Arun²,

Gidwani³

et al. 2020

Radiology: Artificial Intelligence

123

149

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Purpose To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. Materials and Methods A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated. Results PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)). Conclusion A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.

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Section: Discussionmentioning

confidence: 99%

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

Li¹,

Arun²,

Gidwani³

et al. 2020

Radiology: Artificial Intelligence

123

149

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“…Due to the lack of recognized systems for quantitative CXR classification of effusion severity, pleural effusion was arbitrarily defined from 0 to 4 (0 = no effusion; 1 = effusion limited to the costophrenic angles, not above the level of the diaphragm; 2 = effusion above the diaphragm but below the hilum; 3 = effusion extending above the hilum; 4 = effusion in the whole hemithorax) Atelectasis , classified with a score from 0 to 3 as none, mild, moderate, and severe for each lung. Pulmonary interstitial edema, defined as none, moderate, severe, according to recent classifications . An example is illustrated in Figure Diaphragmatic anomalies …”

Section: Methodsmentioning

confidence: 99%

Lung ultrasound reclassification of chest X‐ray data after pediatric cardiac surgery

Cantinotti

Ait-Alì

Scalese

et al. 2018

Pediatric Anesthesia

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“…Automated evaluation of pulmonary edema severity on CXRs has been explored using a deep learning model that incorporates ordinal regression of edema severity labels in training (no, mild, moderate, or severe edema) (22). These severity labels were extracted from associated radiology reports, but are inherently noisy given the variability in interpretation of the CXRs (23,24). This problem of noisy labels extends beyond pulmonary edema to any disease process where there is subjectivity in interpretation.…”

Section: Discussionmentioning

confidence: 99%

Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks

Arun

Gidwani

et al. 2020

Preprint

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Purpose To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease evaluation and clinical risk stratification. Materials and Methods A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on anterior-posterior CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ~160,000 images from CheXpert and transfer learning on 314 CXRs from patients with COVID-19. The algorithm was evaluated on internal and external test sets from different hospitals, containing 154 and 113 CXRs respectively. The PXS score was correlated with a radiographic severity score independently assigned by two thoracic radiologists and one in-training radiologist. For 92 internal test set patients with follow-up CXRs, the change in PXS score was compared to radiologist assessments of change. The association between PXS score and subsequent intubation or death was assessed. Results The PXS score correlated with the radiographic pulmonary disease severity score assigned to CXRs in the COVID-19 internal and external test sets (ρ=0.84 and ρ=0.78 respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operator characteristic curve=0.80 (95%CI 0.75-0.85)). Conclusion A Siamese neural network-based severity score automatically measures COVID-19 pulmonary disease severity in chest radiographs, which can be scaled and rapidly deployed for clinical triage and workflow optimization.

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Improving diagnostic accuracy in assessing pulmonary edema on bedside chest radiographs using a standardized scoring approach

Cited by 24 publications

References 40 publications

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

Lung ultrasound reclassification of chest X‐ray data after pediatric cardiac surgery

Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks

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