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

Jiajie Tang, Jin Han (), Bingbing Xie, Jiaxin Xue, Hang Zhou, Yuxuan Jiang, Lianting Hu, Caiyuan Chen, Kanghui Zhang, Fanfan Zhu and Long Lu ()
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Jiajie Tang: School of Information Management, Wuhan University, Wuhan 430072, China
Jin Han: Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, China
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
Hang Zhou: Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, 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
Caiyuan Chen: Institute of Pediatrics, Prenatal Diagnostic Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510180, 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

IJERPH, 2023, vol. 20, issue 3, 1-16

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.

Keywords: fetal genetic disease; obstetrics and gynecology ultrasound; fetal facial; deep learning; ensemble learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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