The development and validation of an artificial intelligence-based screening method for atrial septal defect in children's chest x-rays.

Purpose: For precise diagnosis and effective management of atrial septal defects, it is of utmost significance to conduct elementary screenings on children. The primary aim of this study is to develop and authenticate an objective methodology for detecting atrial septal defects by employing deep learning (DL) on chest x-ray (CXR) examinations. Methods: This retrospective study encompassed echocardiographs and corresponding Chest x-rays that were consistently gathered at Qingdao Women's and Children's Hospital from 2018 to 2022. Based on a collaborative diagnosis report by two cardiologists with over 10 years of experience in echocardiography, these radiographs were classified as positive or negative for atrial septal defect, and then divided into training and validation datasets. An artificial intelligence model was formulated by utilizing the training dataset and fine-tuned using the validation dataset. To evaluate the efficacy of the model, an assessment of the area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value was conducted employing the validation dataset. Results: This research encompassed a total of 420 images from individuals. The screening accuracy and recall rate of the model surpass 90%. Conclusions: One of profound neural network models predicated on chest x-ray radiographs (a traditional, extensively employed, and economically viable examination) proves highly advantageous in the assessment for atrial septal defect.