دورية أكاديمية
Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate.
| العنوان: | Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate. |
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| المؤلفون: | Huang SG; Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025 China., Qian XS; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 215163 Suzhou, China., Cheng Y; Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025 China., Guo WL; Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025 China., Zhou ZY; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China., Dai YK; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 China. Electronic address: daiyk@sibet.ac.cn. |
| المصدر: | Journal of pediatric surgery [J Pediatr Surg] 2021 Oct; Vol. 56 (10), pp. 1711-1717. Date of Electronic Publication: 2021 May 24. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Saunders Country of Publication: United States NLM ID: 0052631 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5037 (Electronic) Linking ISSN: 00223468 NLM ISO Abbreviation: J Pediatr Surg Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Philadelphia, PA : Saunders Original Publication: New York. |
| مواضيع طبية MeSH: | Barium Enema* , Hirschsprung Disease*/diagnostic imaging, Barium Sulfate ; Early Diagnosis ; Enema ; Humans ; Infant, Newborn ; Machine Learning |
| مستخلص: | Objective: To develop a mathematical model based on a combination of clinical and radiologic features (barium enema) for early diagnosis of short-segment Hirschsprung disease (SHSCR) in neonate. Methods: The analysis included 54 neonates with biopsy-confirmed SHSCR (the cases) and 59 neonates undergoing barium enema for abdominal symptoms but no Hirschsprung disease (the control). Colon shape features extracted from barium enema images and clinical features were used to develop diagnostic models using support vector machine (SVM) and L2-regularized logistic regression (LR). The training cohort included 32 cases and 37 controls; testing cohort consisted 22 cases and 22 controls. Results were compared to interpretation by 2 radiologists. Results: In the analysis by radiologists, 87 out of 113 cases were correctly classified. Six SHSCR cases were mis-classified into the non-HSCR group. In the remaining 20 cases, radiologists were unable to make a decision. Both the SVM and LR classifiers contained five clinical features and four shape features. The performance of the two classifiers was similar. The best model had 86.36% accuracy, 81.82% sensitivity, and 90.91% specificity. The AUC was 0.9132 for the best-performing SVM classifier and 0.9318 for the best-performing LR classifier. Conclusion: A combination of clinical features and colon shape features extracted from barium enemas can be used to improve early diagnosis of SHSCR in neonate. (Copyright © 2021. Published by Elsevier Inc.) |
| فهرسة مساهمة: | Keywords: Early diagnosis; Hirschsprung disease; Machine learning; Quantitative shape features |
| المشرفين على المادة: | 25BB7EKE2E (Barium Sulfate) |
| تواريخ الأحداث: | Date Created: 20210614 Date Completed: 20210915 Latest Revision: 20210915 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1016/j.jpedsurg.2021.05.006 |
| PMID: | 34120738 |
| قاعدة البيانات: | MEDLINE |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 1531-5037 |
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| DOI: | 10.1016/j.jpedsurg.2021.05.006 |