Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.
Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 965 - 976 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678186&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678186 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678186 178678186 178678186 10.1007/s10278-024-00987-1 178678186 ppf: 965 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning. aug: au: Liu, Yiman Huang, Qiming Han, Xiaoxiang Liang, Tongtong Zhang, Zhifang Lu, Xiuli Dong, Bin Yuan, Jiajun Wang, Yan Hu, Menghan Wang, Jinfeng Stefanidis, Angelos Su, Jionglong Chen, Jiangang Li, Qingli Zhang, Yuqi affil: Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China sug: subj: Heart Septal Defects, Atrial Ultrasonography Videorecording Echocardiography Deep Learning Pediatric Care Human Double-Blind Studies Sensitivity and Specificity Predictive Value of Tests Funding Source ab: Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33 ± 3.13 AUC, 84.95 ± 3.88 accuracy, 85.70 ± 4.91 sensitivity, 81.51 ± 8.15 specificity, and 81.99 ± 5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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