Identification of Atrial Septal Defects in Congenital Heart Disease Patients Using Deep Learning-based Chest Radiographs

Objective: Chest X-rays are a non-invasive and cost-effective diagnostic method; however, they are not commonly used for diagnosing atrial septal defects (ASD). Typically, the diagnosis of ASD requires more expensive tests. We have discovered that an enlarged heart shadow can be an initial indicator of ASD. We have developed and validated a model using deep learning techniques to determine whether a patient has an atrial septal defect based on their chest X-ray. Method: This study involved chest X-ray examinations of 1459 patients diagnosed with atrial septal defects at West China Hospital of Sichuan University from January 8, 2009, to June 25, 2022, as well as 1580 individuals who underwent general health check-ups at the West China Hospital's Examination Center from July 21, 2022, to August 13, 2022. Results: This study included chest X-ray examinations of 3,039 patients (44.3% male; median age 33 years with an interquartile range of 28–42 years). For the classification task, the deep learning model achieved an accuracy of 0.96, sensitivity of 0.97, specificity of 0.96, and an AUC (Area Under the Curve) of 0.9922. Conclusion: Current research indicates that chest X-ray analysis based on deep learning can determine the presence of atrial septal defects, a congenital heart condition. These findings suggest that deep learning methods can offer an objective diagnosis for atrial septal defects in routine health screenings, offering a low-cost and non-invasive approach.