Beksaç M S, Durak B, Ozkan O, Cakar A N, Balci S, Karakaş U, Laleli Y
Department of Obstetrics and Gynecology, Hacettepe University, Ankara, Turkey.
Eur J Obstet Gynecol Reprod Biol. 1995 Apr;59(2):131-6. doi: 10.1016/0028-2243(94)02034-c.
To develop an artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health problems by using maternal serum markers ('Triple Test') and maternal age.
A total of 112 pregnant women were referred to Fetal Medicine Unit of Hacettepe University Hospital for fetal ultrasonography and chromosome analysis with different indications. All patients underwent genetic amniocentesis or fetal blood sampling under ultrasound guidance. Gross malformations and hydrops fetalis were detected in 15 and 5 fetuses, respectively. We have found chromosomal abnormality in 7 cases. 'Triple Test' is offered to all patients and serum levels of alpha-fetoprotein, human chorionic gonadotropin and unconjugated estriol were analyzed by radioimmunoassay. In this study, we have used supervised artificial neural network structure to develop a diagnostic system. Our system's input parameters are maternal age, gestational age and 'Triple Test' results. Our system consists of two different artificial neural network modules whose decision-making logics are different. One of them is designed to search genetical disorders while the other one is for the assessment of fetal well-being. Confusion matrix is used for statistical evaluation.
The discriminatory power of the artificial neural network to search genetical disorders and fetal well-being is found to be highly significant (z = 10.583 and z = 10.424, respectively).
This system brings objectively to the evaluation of 'Triple Test' results and can be used both for the detection of genetical disorders and fetal well-being. Nevertheless, the analysis program's performance is limited to input information and knowledge and medical expert expert can not get more than he or she has donated the system.
开发一种带有神经网络的人工智能诊断系统，通过使用母体血清标志物（“三联检测”）和孕妇年龄来确定遗传疾病和胎儿健康问题。
共有112名孕妇因不同指征被转诊至哈杰泰佩大学医院胎儿医学科进行胎儿超声检查和染色体分析。所有患者均在超声引导下接受遗传羊膜穿刺术或胎儿采血。分别在15例和5例胎儿中检测到严重畸形和胎儿水肿。我们发现7例存在染色体异常。所有患者均接受“三联检测”，并通过放射免疫分析法分析甲胎蛋白、人绒毛膜促性腺激素和未结合雌三醇的血清水平。在本研究中，我们使用有监督的人工神经网络结构来开发诊断系统。我们系统的输入参数为孕妇年龄、孕周和“三联检测”结果。我们的系统由两个决策逻辑不同的不同人工神经网络模块组成。其中一个旨在搜索遗传疾病，另一个用于评估胎儿健康状况。使用混淆矩阵进行统计评估。
发现人工神经网络搜索遗传疾病和评估胎儿健康状况的辨别能力非常显著（分别为z = 10.583和z = 10.424）。
该系统客观地用于评估“三联检测”结果，可用于检测遗传疾病和评估胎儿健康状况。然而，分析程序的性能限于输入信息和知识，医学专家从中获得的信息不会超过其提供给系统的信息。
