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The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

Author

Listed:
  • Jiajie Tang

    (School of Information Management, Wuhan University, Wuhan 430072, China
    Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    These authors contributed equally to this work.)

  • Jin Han

    (Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    Graduate School, Guangzhou Medical University, Guangzhou 511436, China
    These authors contributed equally to this work.)

  • Bingbing Xie

    (School of Information Management, Wuhan University, Wuhan 430072, China)

  • Jiaxin Xue

    (Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    Graduate School, Guangzhou Medical University, Guangzhou 511436, China)

  • Hang Zhou

    (Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    Graduate School, Guangzhou Medical University, Guangzhou 511436, China)

  • Yuxuan Jiang

    (Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China)

  • Lianting Hu

    (Medical Big Data Center, Guangdong Provincial People’s Hospital, Guangzhou 510080, China
    Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangzhou 510080, China)

  • Caiyuan Chen

    (Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    Graduate School, Guangzhou Medical University, Guangzhou 511436, China)

  • Kanghui Zhang

    (School of Information Management, Wuhan University, Wuhan 430072, China)

  • Fanfan Zhu

    (School of Information Management, Wuhan University, Wuhan 430072, China)

  • Long Lu

    (School of Information Management, Wuhan University, Wuhan 430072, China
    Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
    Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
    School of Public Health, Wuhan University, Wuhan 430072, China)

Abstract

With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.

Suggested Citation

  • Jiajie Tang & Jin Han & Bingbing Xie & Jiaxin Xue & Hang Zhou & Yuxuan Jiang & Lianting Hu & Caiyuan Chen & Kanghui Zhang & Fanfan Zhu & Long Lu, 2023. "The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2377-:d:1050124
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