Prenatal diagnosis of ultrasound soft markers in a single medical center of mainland China

Chromosomal Abnormalities Detected by Chromosomal Microarray Analysis and Karyotype in Fetuses with Ultrasound Abnormalities

Objective

Chromosomal microarray analysis (CMA) is a first-line test to assess the genetic etiology of fetal ultrasound abnormalities. The aim of this study was to evaluate the effectiveness of CMA in detecting chromosomal abnormalities in fetuses with ultrasound abnormalities, including structural abnormalities and non-structural abnormalities.

Methods

A retrospective study was conducted on 368 fetuses with abnormal ultrasound who received interventional prenatal diagnosis at Meizhou People's Hospital from October 2022 to December 2023. Samples of villi, amniotic fluid, and umbilical cord blood were collected according to different gestational weeks, and karyotype and CMA analyses were performed. The detection rate of chromosomal abnormalities in different ultrasonic abnormalities was analyzed.

Results

There were 368 fetuses with abnormal ultrasound, including 114 (31.0%) with structural abnormalities, 225 (61.1%) with non-structural abnormalities, and 29 (7.9%) with structural combined with non-structural abnormalities. The detection rate of aneuploidy and pathogenic (P)/likely pathogenic (LP) copy number variations (CNVs) of CMA in fetuses with structural abnormalities was 5.26% (6/114), the detection rate of karyotype was 2.63% (3/114), and the additional diagnosis rate of CMA was 2.63%. In the fetuses with ultrasonic non-structural abnormalities, the detection rate of karyotype was 6.22% (14/225), the detection rate of aneuploidy and P/LP CNVs in fetuses with ultrasonic structural abnormalities was 9.33% (21/225), and the additional diagnosis rate of CMA was 3.11%. There was no significant difference in chromosome abnormality detection rate of CMA among structural abnormality, non-structural abnormality, and structural abnormality combined with non-structural abnormality groups (5.3%, 9.3%, and 13.8%, p = 0.241), also among multiple ultrasonic abnormality and single ultrasonic abnormality groups (14.8%, and 7.3%, p = 0.105).

Conclusion

CMA can significantly improve the detection rate of genetic abnormalities in prenatal diagnosis of ultrasonic abnormal fetuses compared with karyotype analysis. CMA is a more effective tool than karyotyping alone in detecting chromosomal abnormalities in fetuses with ultrasound abnormalities.

Chromosomal Microarray Analysis in Fetuses with Ultrasound Abnormalities

Objective

To explore and evaluate the value of chromosomal microarray analysis (CMA) in prenatal diagnosis of fetuses with ultrasound abnormalities.

Methods

A retrospective analysis was performed on 370 fetuses with ultrasound abnormalities received invasive prenatal diagnosis at Meizhou People's Hospital from October 2022 to December 2023. Fetal specimens were analyzed by CMA, and the detection rates of aneuploidy and pathogenic (P)/likely pathogenic (LP) copy number variations (CNVs) in ultrasound structural abnormalities (malformations of fetal anatomy) and non-structural abnormalities (abnormalities of fetal nonanatomical structure) were analyzed.

Results

There were 114 (30.8%) cases with isolated ultrasound structural abnormalities, 226 (61.1%) cases with isolated non-structural abnormalities (182 isolated ultrasound soft markers abnormalities, 30 isolated fetal growth restriction (FGR), and 8 isolated abnormalities of amniotic fluid volume), and 30 (8.1%) cases with both structural and non-structural abnormalities. The overall detection rate of aneuploidy and P/LP CNVs in isolated ultrasonic structural abnormalities was 5.3%, among which cardiovascular system abnormalities were the highest. In addition, the largest number of fetuses with non-structural abnormalities was nuchal translucency (NT) thickening (n = 81), followed by ventriculomegaly (n = 29), and nasal bone dysplasia (n = 24). The detection rate of chromosomal abnormalities of fetuses with abnormal ultrasound soft markers was 9.9%, and the detection rate in single abnormal ultrasound soft marker, and multiple ultrasound soft markers abnormalities was 9.7% (16/165) and 11.8% (2/17), respectively. Moreover, the detection rate of chromosomal abnormalities of fetuses with FGR and structural abnormalities combined with non-structural abnormalities was 6.7% (2/30), and 13.3% (4/30), respectively.

Conclusion

The incidence of chromosomal abnormalities (aneuploidy and P/LP CNVs) varies among different fetal ultrasound abnormalities.

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.