Pulmonary vasculature development in congenital diaphragmatic hernia: a novel automated quantitative imaging analysis.

PURPOSE: Impaired fetal lung vasculature determines the degree of pulmonary hypertension in the congenital diaphragmatic hernia (CDH). This study aims to demonstrate the morphometric measurements that differ in pulmonary vessels of fetuses with CDH. METHODS: Nitrofen-induced CDH Sprague-Dawley rat fetuses were scanned with microcomputed tomography. The analysis of the pulmonary vascular tree was performed with artificial intelligence. RESULTS: The number of segments in CDH was significantly lower than that in the control group on the left (U = 2.5, p = 0.004) and right (U = 0, p = 0.001) sides for order 1(O1), whereas there was a significant difference only on the right side for O2 and O3. The pooled element numbers in the control group obeyed Horton's law (R2 = 0.996 left and R2 = 0.811 right lungs), while the CDH group broke it. Connectivity matrices showed that the average number of elements of O1 springing from elements of O1 on the left side and the number of elements of O1 springing from elements of O3 on the right side were significantly lower in CDH samples. CONCLUSION: According to these findings, CDH not only reduced the amount of small order elements, but also destroyed the fractal structure of the pulmonary arterial trees.

A simple machine learning approach for preoperative diagnosis of esophageal burns after caustic substance ingestion in children.

PURPOSE: The unresolved debate about the management of corrosive ingestion is a major problem both for the patients and healthcare systems. This study aims to demonstrate the presence and the severity of the esophageal burn after caustic substance ingestion can be predicted with complete blood count parameters. METHODS: A multicenter, national, retrospective cohort study was performed on all caustic substance cases between 2000 and 2018. The classification learner toolbox of MATLAB version R2021a was used for the classification problem. Machine learning algorithms were used to forecast caustic burn. RESULTS: Among 1839 patients, 142 patients (7.7%) had burns. The type of the caustic and the PDW (platelet distribution width) values were the most important predictors. In the acid group, the AUC (area under curve) value was 84% while it was 70% in the alkaline group. The external validation had 85.17% accuracy in the acidic group and 91.66% in the alkaline group. CONCLUSIONS: Artificial intelligence systems have a high potential to be used in the prediction of caustic burns in pediatric age groups.