Residual networks models detection of atrial septal defect from chest radiographs

Abstract

Object The purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on chest radiographs. Methods This retrospective study included chest radiographs consecutively collected at our hospital from June 2017 to May 2022. Qualified chest radiographs were obtained from patients who had finished echocardiography. These chest radiographs were labeled as positive or negative for ASD based on the echocardiographic reports and were divided into training, validation, and test dataset. Six ResNets models were employed to examine and compare by using the training dataset and was tuned using the validation dataset. The area under the curve, recall, precision and F1-score were taken as the evaluation metrics for classification result in the test dataset. Visualizing regions of interest for the ResNets models using heat maps. Results This study included a total of 2105 chest radiographs of children with ASD (mean age 4.14 ± 2.73 years, 54% male), patients were randomly assigned to training, validation, and test dataset with an 8:1:1 ratio. Healthy children’s images were supplemented to three datasets in a 1:1 ratio with ASD patients. Following the training, ResNet-10t and ResNet-18D have a better estimation performance, with precision, recall, accuracy, F1-score, and the area under the curve being (0.92, 0.93), (0.91, 0.91), (0.90, 0.90), (0.91, 0.91) and (0.97, 0.96), respectively. Compared to ResNet-18D, ResNet-10t was more focused on the distribution of the heat map of the interest region for most chest radiographs from ASD patients. Conclusion The ResNets model is feasible for identifying ASD through children’s chest radiographs. ResNet-10t stands out as the preferable estimation model, providing exceptional performance and clear interpretability.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

“…CT is the main imaging method used to characterize SRMs, since it has an easy accessibility and high tolerability by patients [53][54][55][56]. However, since this technology is based on ionizing radiation, it should be chosen with caution for young patients and pregnant women and mostly used for lesion surveillance.…”