Machine learning-based quantitative analysis of barium enema and clinical features for early diagnosis of short-segment Hirschsprung disease in neonate

Abstract

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.

Keywords: Early diagnosis; Hirschsprung disease; Machine learning; Quantitative shape features.