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.
Int J Environ Res Public Health. 2023 Jan 29;20(3):2377. doi: 10.3390/ijerph20032377.
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.
近年来，随着医学的进步，越来越多的研究人员将注意力转向胎儿遗传疾病的研究。然而，在医疗资源匮乏的地区，检测胎儿的遗传疾病仍然是一个挑战。现有的研究主要集中在利用青少年或成年人的面部信息来筛查遗传疾病，但在利用胎儿面部信息进行疾病检测方面尚无相关方向。为了填补这一空白，我们设计了一个基于超声的两级集成学习模型 Fgds-EL，使用 932 张图像来识别遗传疾病。具体来说，我们使用面部区域的聚合信息来检测异常，如下颌、额骨和鼻骨区域。我们的实验表明，我们的模型在测试集上的灵敏度为 0.92，特异性为 0.97，与高级超声医师相当，优于其他流行的深度学习算法。此外，我们的模型有可能成为一种有效的非侵入性筛查工具，用于早期筛查胎儿的遗传疾病。
