An explainable deep learning model to predict partial anomalous pulmonary venous connection for patients with atrial septal defect.

Journal: BMC pediatrics
PMID: 39516757

Abstract

BACKGROUND: Patients with partial anomalous pulmonary venous connection (PAPVC) usually present asymptomatic and accompanied by intricate anatomical types, which results in missed diagnosis from atrial septal defect (ASD). The present study aimed to explore the predictive variables of PAPVC from patients with ASD and constructed an explainable prediction model based on deep learning.

Authors

  • Gang Luo
    Department of Biomedical Informatics and Medical Education, University of Washington UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047 Seattle, WA 98195, USA, luogang@uw.edu.
  • Zhixin Li
    School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China.
  • Zhixian Ji
    Heart Center, Women and Children's Hospital, Qingdao University, 6 Tongfu Road, Qingdao, 266034, China.
  • Sibao Wang
    Heart Center, Women and Children's Hospital, Qingdao University, 6 Tongfu Road, Qingdao, 266034, China.
  • Silin Pan
    Heart Center, Women and Children's Hospital, Qingdao University, 6 Tongfu Road, Qingdao, 266034, China. silinpan@126.com.

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