Automated Analysis of Chest Radiographs for Cystic Fibrosis Scoring

We present a framework to analyze chest radiographs for cystic fibro-sis using machine learning methods. We compare the representational power of deep learning features with traditional texture features. Specifically, we respec-tively employ VGG-16 based deep learning features, Tamura and Gabor filter based textural features to represent the cystic fibrosis images. We demonstrate that VGG-16 features perform best, with a maximum agreement of 82%. In ad-dition, due to limited dimensionality, Tamura features for unsegmented images achieve no more than 50% agreement; however, after segmentation, the accuracy of Tamura can reach 78%. In combination with using the deep learning features, we also compare back propagation neural network and sparse coding classifiers to the typical SVM classifier with polynomial kernel function. The result shows that neural network and sparse coding classifiers outperform SVM in most cases. Only with insufficient training samples does SVM demonstrate higher accuracy

Chest computed tomography in severe bronchopulmonary dysplasia:Comparing quantitative scoring methods

Purpose: Bronchopulmonary dysplasia (BPD) is the most common complication of extreme preterm birth and structural lung abnormalities are frequently found in children with BPD. To quantify lung damage in BPD, three new Hounsfield units (HU) based chest-CT scoring methods were evaluated in terms of 1) intra- and inter-observer variability, 2) correlation with the validated Perth-Rotterdam-Annotated-Grid-Morphometric-Analysis (PRAGMA)-BPD score, and 3) correlation with clinical data. Methods: Chest CT scans of children with severe BPD were performed at a median of 7 months corrected age. Hyper- and hypo-attenuated regions were quantified using PRAGMA-BPD and three new HU based scoring methods (automated, semi-automated, and manual). Intra- and inter-observer variability was measured using intraclass correlation coefficients (ICC) and Bland-Altman plots. The correlation between the 4 scoring methods and clinical data was assessed using Spearman rank correlation. Results: Thirty-five patients (median gestational age 26.1 weeks) were included. Intra- and inter-observer variability was excellent for hyper- and hypo-attenuation regions for the manual HU method and PRAGMA-BPD (ICCs range 0.80–0.97). ICC values for the semi-automated HU method were poorer, in particular for the inter-observer variability of hypo- (0.22–0.71) and hyper-attenuation (-0.06–0.89). The manual HU method was highly correlated with PRAGMA-BPD score for both hyper- (ρs0.92, p &lt; 0.001) and hypo-attenuation (ρs0.79, p &lt; 0.001), while automated and semi-automated HU methods showed poor correlation for hypo- (ρs &lt; 0.22) and good correlation for hyper-attenuation (ρs0.72–0.74, p &lt; 0.001). Several scores of hyperattenuation correlated with the use of inhaled bronchodilators in the first year of life; two hypoattenuation scores correlated with birth weight. Conclusions: PRAGMA-BPD and the manual HU method have the best reproducibility for quantification of CT abnormalities in BPD.</p

Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs

Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult chest radiographs can be used for pediatric chest radiographs. Pediatric patients (≤ 18 years old) who underwent chest radiographs from March to May 2021 were included retrospectively. An AI-based lesion detection software assessed the presence of nodules, consolidation, fibrosis, atelectasis, cardiomegaly, pleural effusion, pneumothorax, and pneumoperitoneum. Using the pediatric radiologist's results as standard reference, we assessed the diagnostic performance of the software. For the total 2273 chest radiographs, the AI-based software showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 67.2%, 91.1%, 57.7%, 93.9%, and 87.5%, respectively. Age was a significant factor for incorrect results (odds radio 0.821, 95% confidence interval 0.791-0.851). When we excluded cardiomegaly and children 2 years old or younger, sensitivity, specificity, PPV, NPV and accuracy significantly increased (86.4%, 97.9%, 79.7%, 98.7% and 96.9%, respectively, all p < 0.001). In conclusion, AI-based software developed with adult chest radiographs showed diagnostic accuracies up to 96.9% for pediatric chest radiographs when we excluded cardiomegaly and children 2 years old or younger. AI-based lesion detection software needs to be validated in younger children.ope

Monitoring of Cystic Fibrosis Lung Disease Using Computed Tomography

For clinical management of cystic fibrosis (CF) lung disease to be effective, onset and worsening of lung abnormalities should be closely monitored. Pulmonary function tests (PFTs) are currently the gold standard to monitor CF lung disease. Lung structure can be more sensitively monitored using computed tomography (CT) rather than chest radiography. Firstly, we compared in two pediatric cohorts the sensitivities of CT and PFTs to detect onset and worsening of CF lung disease. We showed that five published CT scoring systems are comparable and have good intra- and interobserver agreement. Secondly, we showed that CT scoring systems and quantitative CT-measurements of airway wall thickening and bronchiectasis are more sensitive to detect the start and worsening of CF lung disease than are PFTs. Bronchiectasis-score worsened most in children and the worsening remained undetected by the PFTs and the quantitative measurements. Quantitative CT-measurements of air! way wall thickening worsened significantly, whereas PFTs and airway wall thickness measured by scoring remained unchanged. Thirdly, we developed a computational model to study radiation risks associated with CT scanning in CF. Risks from lifelong biennial CT scanning in CF were found to be acceptably low given the currently reduced life expectancy. Finally we provided normal CT-values of lung parenchyma and airway wall and lumen that can be used to study lung growth aberrations due to CF. Our data support routine CT scanning to monitor CF lung disease. In addition, bronchiectasis-score and quantitatively measured airway wall thickening may be useful surrogate endpoints for clinical trials in CF

Imaging and Treatment of Bronchiectasis:Chest computed tomography to diagnose bronchiectasis and to optimise inhalation treatment

This thesis covers image analysis of bronchiectasis and treatment with inhalation antibiotics

Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

Using artificial intelligence in fungal lung disease: CPA CT imaging as an example

This positioning paper aims to discuss current challenges and opportunities for artificial intelligence (AI) in fungal lung disease, with a focus on chronic pulmonary aspergillosis and some supporting proof-of-concept results using lung imaging. Given the high uncertainty in fungal infection diagnosis and analyzing treatment response, AI could potentially have an impactful role; however, developing imaging-based machine learning raises several specific challenges. We discuss recommendations to engage the medical community in essential first steps towards fungal infection AI with gathering dedicated imaging registries, linking with non-imaging data and harmonizing image-finding annotations