Dual-source computed tomography image information under deep learning algorithm in evaluation of coronary artery lesion in children with Kawasaki disease

2021 ◽  
Author(s):  
Pan Luo ◽  
Jinling Li
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Tomography Image ◽  
Dual Source ◽  
Computed Tomography Image ◽  
Dual Source Computed Tomography

Explainable Deep Learning Algorithm for Distinguishing Incomplete Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging (Preprint)

2021 ◽  
Author(s):  
Haeyun Lee ◽  
Yongsoon Eun ◽  
Jae Youn Hwang ◽  
Lucy Youngmin Eun
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Learning Algorithm ◽  
Clinical Manifestations ◽  
Learning Networks ◽  
Coronary Artery Lesions ◽  
Incomplete Kawasaki Disease ◽  
Deep Learning Algorithm

BACKGROUND Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. OBJECTIVE In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. METHODS We obtained coronary artery images by echocardiography of children (n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. RESULTS SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a sensitivity of 82.64% and a specificity of 58.12%. CONCLUSIONS The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD. CLINICALTRIAL none

Measurements of Coronary Artery Aneurysms Due to Kawasaki Disease by Dual-Source Computed Tomography (DSCT)

2015 ◽  
Vol 37 (3) ◽  
pp. 442-447 ◽  
Author(s):  
Nobuyuki Tsujii ◽  
Etsuko Tsuda ◽  
Suzu Kanzaki ◽  
Kenichi Kurosaki
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Coronary Artery Aneurysms ◽  
Dual Source ◽  
Dual Source Computed Tomography

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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