The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases
Open accessAbstract
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.
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Tang et al. used 3D images of fetal faces to develop a novel approach for the early, non-invasive identification of genetic disorders by analyzing key facial regions, such as the jaw, frontal bone, and nasal bone.
“Tang et al. used 3D images of fetal faces to develop a novel approach for the early, non-invasive identification of genetic disorders by analyzing key facial regions, such as the jaw, frontal bone, and nasal bone [99].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [99]
The Abstract says Tang et al. developed a sonography-based model for early, noninvasive screening of fetal genetic diseases using the jaw, frontal bone, and nasal bone regions. However, Methods §2.1 describes the collected images as fetal ultrasound profiles, and §2.3.1 describes pixel-based subgraphs extracted from those images. Although §2.1 says cases without 3D volume images were excluded, the passages do not establish that the model analyzed 3D facial images. (LLM, conf=0.85)
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References 37 · Cited by 13
Research works printed but not linked: 4
- [22]Schmidt-Richberg A. Brosch T. Schadewaldt N. Klinder T. Cavallaro A. Salim I. Roundhill D. Papageorghiou A. Lorenz C. Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-Powered Deformable Models Fetal, Infant and Ophthalmic Medical Image Analysis Springer Berlin/Heidelberg, Germany 2017 52 61
- [31]Simonyan K. Zisserman A. Very deep convolutional networks for large-scale image recognition arXiv 2014 1409.1556
- [35]Li Y. Yuan G. Wen Y. Hu J. Evangelidis G. Tulyakov S. Wang Y. Ren J. EfficientFormer: Vision Transformers at MobileNet Speed arXiv 2021 2206.01191
- [39]Cnota W. Borowski D. Wloch A. Pomiar kąta twarzowo-szczękowego u płodów pomiędzy 11+ 0 a 13+ 6 tygodniem ciąży. Zastosowanie w diagnostyce prenatalnej trisomii 21 Ginekol. Pol. 2013 84 624 629 24032275
- Authors
- Tang, J., Han, J., Xie, B., Xue, J., Zhou, H., Jiang, Y., Hu, L., Chen, C., Zhang, K., Zhu, F., & Lu, L.
- Affiliations
- Wuhan University (CN)
- Guangzhou Women and Children Medical Center (CN)
- Guangdong Provincial People's Hospital (CN)
- Corresponding author
- Han, Jin
- Lu, Long
- ORCID
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-01-29
- Volume
- 20
- Issue
- 3
- Pages
- 2377
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC9914999
- OpenAlex
- W4318475120
- Cited by
- 13
- References
- 37
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 2
- FWCI
- 4.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (61772375, 61936013, 71921002, 202201010824, 20221800905042, 2021A1515220070)
- Basic and Applied Basic Research Project of Guangzhou Municiple Science and Technology Bureau (61772375, 61936013, 71921002, 202201010824, 20221800905042, 2021A1515220070)
- Key Program for Dongguan Science and Technology Foundation (61772375, 61936013, 71921002, 202201010824, 20221800905042, 2021A1515220070)
- GuangDong Basic and Applied Basic Research Foundation (61772375, 61936013, 71921002, 202201010824, 20221800905042, 2021A1515220070)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Keywords
- prenatal screening, ultrasonography, jaw, frontal bone, nasal bone, non-invasive screening, low-resource healthcare
- MeSH terms
- Machine Learning, Adolescent, Adult, Face, Female, Fetus, Humans, Pregnancy, Prenatal Care, Ultrasonography
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