Verma Deepti, Agrawal Shweta, Iwendi Celestine, Sharma Bhisham, Bhatia Surbhi, Basheer Shakila
Department of Computer Application, SAGE University, Indore 452020, India.
Institute of Advance Computing, SAGE University, Indore 452020, India.
Diagnostics (Basel). 2022 Oct 31;12(11):2643. doi: 10.3390/diagnostics12112643.
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.
在大多数妇产医院，孕中期超声扫描如今已成为产前护理的标准项目。随着技术进步和能力提升，扫描中检测出的胎儿异常情况越来越多。胎儿异常是指胎儿在孕期出现的发育异常，出生缺陷和先天性异常是相关术语。在过去几十年里，工业化国家普遍观察到胎儿异常情况。每1000名怀孕母亲中有3人会遭遇胎儿异常。这项研究工作提出了一种自适应随机梯度下降算法来评估胎儿异常风险。这项工作的研究结果表明，所提出的创新方法能够成功对与颈部半透明增厚相关的异常情况进行分类。分析了诸如准确率、召回率、精确率和F1分数等参数。通过所建议技术实现的准确率为98.642%。
