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

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.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

“…These challenges can be mitigated through the incorporation of techniques such as regularization parameter tuning and the strategic use of skip connections, as exemplified by ResNets. The inclusion of skip connections not only alleviates the vanishing gradient problem but also [58,[95][96][97][98][99][100][101][102][103]107,109,[115][116][117]120,[124][125][126][127][128][129][130][135][136][137]141,142,145,150,153,157,160,161,169]. Each study's color code reflects its relevance to a certain organ, including the heart, brain, lung, or analyzing for chromosomal abnormalities.…”

“…Each entry provides information about the employed methods, total number of images, key performance metrics, and application domain. [58,[95][96][97][98][99][100][101][102][103]107,109,[115][116][117]120,[124][125][126][127][128][129][130][135][136][137]141,142,145,150,153,157,160,161,169]. Each study's color code reflects its relevance to a certain organ, including the heart, brain, lung, or analyzing for chromosomal abnormalities.…”

“…The CNN was trained to extract high-level features from the facial images, while the RF was used to classify the extracted features and make the final diagnosis. The proposed model achieved a sensitivity of 0.92 and a specificity of 0.97 in the test set [145].…”

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

“…Wang et al and Yu et al presented AI algorithms to automatically identify standard planes in 2D images [97,98]. However, as facial malformations can be a phenotype of an underlying genetic disorder, 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].…”