Abstract

The use of machine learning (ML) in biomarker analysis for predicting Down syndrome exemplifies an innovative strategy that enhances diagnostic accuracy and enables early detection. Recent studies demonstrate the effectiveness of ML algorithms in identifying genetic variations and expression patterns associated with Down syndrome by comparing genomic data from affected individuals and their typically developing peers. This review examines how ML and biomarker analysis improve prenatal screening for Down syndrome. Advancements show that integrating maternal serum markers, nuchal translucency measurements, and ultrasonographic images with algorithms, such as random forests and deep learning convolutional neural networks, raises detection rates to above 85% while keeping false positive rates low. Moreover, non-invasive prenatal testing with soft ultrasound markers has increased diagnostic sensitivity and specificity, marking a significant shift in prenatal care. The review highlights the importance of implementing robust screening protocols that utilize ultrasound biomarkers, along with developing personalized screening tools through advanced statistical methods. It also explores the potential of combining genetic and epigenetic biomarkers with ML to further improve diagnostic accuracy and understanding of Down syndrome pathophysiology. The findings stress the need for ongoing research to optimize algorithms, validate their effectiveness across diverse populations, and incorporate these cutting-edge approaches into routine clinical practice. Ultimately, blending advanced imaging techniques with ML shows promise for enhancing prenatal care outcomes and aiding informed decision-making for expectant parents.

Introduction

The integration of artificial intelligence in medicine is transforming diagnostics and personalized treatment plans by facilitating accurate and efficient disease identification through advanced machine learning (ML)^{(1, 2)}. The use of ML in biomarker analysis for predicting Down syndrome exemplifies this innovative approach, enhancing diagnostic accuracy and enabling early detection. Recent studies demonstrate the effectiveness of ML algorithms in identifying genetic variations and expression patterns associated with Down syndrome by comparing genomic data from affected individuals to typically developing counterparts^(3-5). In prenatal diagnosis, ML techniques have significantly improved early Down syndrome assessment through the analysis of fetal ultrasound images, with deep learning (DL) architectures surpassing traditional methods in recognizing phenotypic traits indicative of Down syndrome^{(6, 7)}. Additionally, integrating various biomarkers from maternal serum screening has strengthened predictive models, enabling comprehensive risk assessments during pregnancy. Facial recognition technology, powered by DL, has emerged as a non-invasive diagnostic tool to identify subtle phenotypic traits characteristic of Down syndrome, showcasing ML’s capability to detect complex patterns beyond human recognition^{(8, 9)}. The reliability of these ML models depends heavily on robust datasets that accurately reflect the target population, as shifts in datasets can greatly impact prediction effectiveness^(10-12). Incorporating multi-omics data, including genomic, transcriptomic, and proteomic information, further enhances the identification of relevant biomarkers, leading to more accurate and holistic diagnostic approaches for Down syndrome^{(13, 14)}. The advantages of this integration include improved detection rates, personalized care through extensive data analysis, and cost-effectiveness by reducing the need for invasive procedures. However, challenges such as data imbalance, the necessity for comprehensive datasets, and ethical concerns regarding privacy and consent must be addressed to fully integrate ML into routine clinical practice for Down syndrome screening^{(15, 16)}.

This review summarizes current findings on integrating ML techniques with biomarker analysis to improve prenatal diagnostics for Down syndrome. It highlights the effectiveness of combining maternal serum markers, ultrasound imaging, and non-invasive prenatal testing (NIPT) with advanced algorithms such as random forests and DL. The review also explores the impact of genetic and epigenetic biomarkers, particularly transcriptomic and methylation profiling, on enhancing diagnostic accuracy for Down syndrome. Moreover, it evaluates personalized screening approaches, including tailored nomograms and predictive models, within clinical practice. By outlining recent advancements, the article aims to identify knowledge gaps, suggest future research directions, and emphasize the importance of incorporating these methodologies into routine prenatal care to enhance outcomes for expectant mothers and their infants.

Machine Learning Models

ML techniques are revolutionizing the prediction and screening of Down syndrome, particularly in prenatal settings, by enhancing detection rates while minimizing false positives^{(4, 17)}. Various studies have demonstrated the effectiveness of different algorithms, such as random forest models, which achieve an impressive 85.2% detection rate with only a 5% false positive rate, significantly outperforming traditional laboratory models⁽¹⁸⁾. Additionally, support vector machines (SVM) and advanced classification algorithms, alongside techniques like SMOTE-Tomek for data preprocessing, have maintained high detection rates⁽¹⁹⁾. DL methods, including Gaussian Processes for neuroimaging data analysis and convolutional neural networks for identifying genetic markers, have further pushed the boundaries of prediction accuracy⁽²⁰⁾. Noteworthy applications in research highlight the role of artificial intelligence and ML in recognizing specific genetic variations, as seen in their work, and fostering improved early detection through models developed for different pregnancy trimesters, as shown by Leghari et al.⁽²¹⁾ and He et al.⁽¹⁸⁾. Additionally, the efficacy of dense neural networks in ultrasound imaging, presented by Yousefpour Shahrivar et al.⁽³⁾, showcases their superiority over traditional methods, while Qin et al.⁽²²⁾ highlight the potential for automated diagnosis through facial image analysis. The exploration of supervised learning algorithms, as emphasized by Feng et al.⁽²³⁾, also plays a crucial role in identifying biomarkers related to Down syndrome, while Li et al.⁽²⁴⁾ introduce a cascaded ML framework that addresses challenges of imbalanced data, offering a novel approach to enhancing prediction accuracy in Down syndrome cases.

He et al.⁽¹⁸⁾ developed a ML model using random forest algorithms, to enhance Down syndrome prediction during second trimester antenatal screening, based on a retrospective analysis of data from 58.972 pregnant women, including 49 confirmed Down syndrome cases. The model achieved a Down syndrome detection rate of 66.7% with a 5% false positive rate in the initial dataset. When validated against an external dataset of 27.170 women, the detection rate improved to 85.2%, indicating its superiority over traditional lab risk models in China and its strong generalizability⁽¹⁸⁾. In another study, Xu et al.⁽²⁵⁾ explored the effectiveness of combining soft ultrasound markers (USM) with NIPT for diagnosing fetal chromosomal abnormalities, analyzing data from 856 high-risk pregnancies. Their findings showed that 15.07% of fetuses had one positive USM and 4.21% had two or more, with an overall chromosomal abnormality detection rate of 9.46%. Notably, multiple USMs correlated with a significantly higher incidence of abnormalities (36.11%) compared to those without USM (6.22%) and those with one positive USM (19.38%). The combination approach yielded diagnostic sensitivity, specificity, and accuracy of 96.72%, 98.45%, and 98.29%, respectively, highlighting its clinical value⁽²⁵⁾. Zhang et al.⁽²⁶⁾ devised a DL model for trisomy 21 screening during the first trimester, utilizing nuchal ultrasonographic images from 822 participants across two Chinese hospitals. Their convolutional neural network achieved impressive areas under the curves (AUCs) of 0.98 and 0.95 for training and validation sets, surpassing traditional methods that yielded AUCs of 0.82 and 0.73. Moreover. Sun et al.⁽²⁷⁾ created an individualized nomogram for first-trimester screening of trisomy 21, utilizing fetal nuchal translucency (NT) thickness and various ultrasonographic facial markers. They analyzed 302 trisomy 21 cases and 322 euploid pregnancies, achieving AUC values of 0.983 in the training set and 0.979 in the validation set using the LASSO method, indicating strong predictive capability. In a study by Neocleous et al.⁽²⁸⁾, they sought to innovate non-invasive diagnostic procedures for aneuploidy using artificial neural networks trained on raw data from first trimester screenings of singleton pregnancies. With three datasets totaling 122.362 euploid and 967 aneuploid cases, the authors’ models achieved a detection rate of 100% for trisomy 21, alongside detection rates exceeding 80% for other aneuploidies such as trisomies 13 and 18. This research showcases the potential of artificial neural networks to provide effective,  non-invasive early screening tools that rival existing methodologies while addressing the social and financial burdens associated with prenatal testing.

Ultrasound Markers

The integration of ML techniques with ultrasound biomarkers offers a promising enhancement to the prediction and screening of Down syndrome during pregnancy. This innovative approach utilizes advanced computational methods to analyze complex data sets, improving detection rates and minimizing false positives^{(4, 19)}. Key USMs, such as NT, and additional indicators, such as the presence or absence of the nasal bone, significantly contribute to risk assessment, with NT alone detecting Down syndrome in 60% to 70% of cases at a 5% false positive rate⁽²⁹⁾. By combining ultrasound data with maternal serum biomarkers, such as pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (βhCG), ML algorithms, enhance detection rates to approximately 87% at the same false positive threshold⁽³⁰⁾. Moreover, sophisticated predictive models incorporating multiple markers can reach detection rates of nearly 90%, offering improved accuracy over traditional methods^{(29, 30)}. This integration not only leads to enhanced screening precision but also reduces the need for invasive testing, such as amniocentesis, and allows for personalized screening strategies tailored to individual risk profiles, ultimately benefiting both mothers and infants.

Xu et al.⁽²⁵⁾ examined the effectiveness of combining soft USM with NIPT for diagnosing fetal chromosomal abnormalities using ML techniques. In a study involving 856 high-risk single pregnancies, NIPT was performed on 642 patients, all of whom also underwent amniocentesis and chromosomal karyotype analysis, to validate the diagnostic performance of USM, Down’s syndrome screening, and NIPT. The results indicated that 15.07% of fetuses had one positive USM and 4.21% had two or more, resulting in an overall detection rate of 9.46% for chromosomal abnormalities. Importantly, multiple USMs correlated with a significantly higher incidence of abnormalities (36.11%) compared to no USMs (6.22%) and one positive USM (19.38%). The integration of USMs, Down’s syndrome screening, and NIPT achieved high diagnostic sensitivity (96.72%), specificity (98.45%), and accuracy (98.29%), underscoring the value of this multimodal approach for improving the detection of fetal chromosomal anomalies⁽²⁵⁾. In a separate study, Sun et al.⁽²⁷⁾ developed and validated a personalized nomogram for first-trimester screening of trisomy 21, using fetal NT thickness and various facial markers. Their retrospective case-control study involved analyzing two-dimensional midsagittal fetal profile images from 302 trisomy 21 cases and 322 euploid pregnancies, which were divided into training and validation sets. Using the least absolute shrinkage and selection operator (LASSO) method, they incorporated eight significant markers into a logistic regression model. The LASSO model demonstrated impressive area under the receiver-operating characteristic curve (AUC) values of 0.983 for the training set and 0.979 for the validation set, surpassing individual marker performance. Moreover, the nomogram showed strong discrimination capabilities, with C-indices of 0.983 and 0.981 for the training and validation sets, respectively. This study highlights the nomogram’s potential as an effective tool for early trisomy 21 screening, providing a tailored risk assessment for expectant mothers⁽²⁷⁾.

Genetic and Epigenetic Biomarkers

The integration of ML techniques with genetic and epigenetic biomarkers represents a groundbreaking approach to enhancing the prediction and diagnosis of Down syndrome^{(25, 31)}. This strategy utilizes advanced computational methods to analyze intricate biological data, which improves detection rates and offers insights into the condition’s underlying mechanisms. At the genetic level, the presence of an extra copy of chromosome 21 is the primary cause of Down syndrome, resulting in an increased expression of key genes such as the amyloid precursor protein, which is linked to Alzheimer’s disease pathology in individuals with Down syndrome as they age. Furthermore, genetic variants such as the ApoE ε4 allele are critical for assessing Alzheimer’s risk, as they are associated with cognitive decline and amyloid accumulation. On the epigenetic front, modifications that drive neuroinflammation and specific proteomic changes in cerebrospinal fluid serve as potential biomarkers for cognitive decline and Alzheimer’s onset^{(32, 33)}. ML applications, including predictive modeling using algorithms like SVM and neural networks, can analyze extensive datasets comprising genetic markers, epigenetic profiles, and clinical information to enhance detection rates significantly. Data mining techniques further reveal hidden correlations between biomarkers and patient outcomes, which can improve screening protocols⁽¹⁰⁾. Laufer et al.⁽³⁴⁾ utilized low-pass whole genome bisulfite sequencing (WGBS) to analyze DNA methylation profiles in neonatal dried blood spots (NDBS) related to Down syndrome. They highlighted that trisomy 21 leads to both genetic alterations and significant epigenetic changes, resulting in unique methylation patterns. Analyzing over 24 million CpG sites, the authors identified thousands of differentially methylated regions that differentiate Down syndrome from typical development and idiopathic developmental delay. Through ML refinement, they focused on 22 loci, primarily linked to genes vital for neurodevelopment, metabolism, and transcriptional regulation. Notably, the RUNX1 locus on chromosome 21 showed a ~28 kb hypermethylation region, emphasizing its role in the epigenomic dysregulation in Down syndrome. The study also explored the connection between differentially methylated regions (DMRs) and congenital heart disease in Down syndrome NDBS, advocating for the use of low-pass WGBS in epigenome investigations, and enhancing understanding of trisomy 21’s early mechanistic pathways influencing epigenomic changes⁽³⁴⁾. Volk et al.⁽³¹⁾ investigated gene expression signatures as biomarkers for the prenatal diagnosis of trisomy 21. Noting the absence of a universal biomarker panel for high-risk pregnancies, they conducted a comprehensive transcriptome analysis to identify differentially expressed genes (DEGs) associated with Ts21. By profiling transcriptomic data from cultivated amniocyte samples of both Ts21 and normal euploid cases, they validated findings through reverse transcription polymerase chain reaction on a larger cohort and included gene expression omnibus repository datasets. Using a supervised ML algorithm, they assessed the classification performance of the Ts21 status, achieving significant results with an AUC of 0.97 for a multi-gene biomarker comprising nine gene expression profiles⁽³¹⁾. These findings reinforce the potential of transcriptomic alterations as diagnostic tools in prenatal settings, applicable to a wider range of genetic disorders stemming from cellular disturbances.

Integrating ML and Biomarkers Across Trimesters

Integrating ML techniques with genetic, epigenetic, and ultrasound biomarkers across trimesters represents a significant advancement in the screening and prediction of Down syndrome. This approach enhances detection rates while reducing false positives by analyzing complex datasets with advanced computational methods. In the first trimester, biomarkers such as NT and maternal serum markers, including PAPP-A and free βhCG, achieve detection rates around 85% when combined with maternal age. The second trimester benefits from additional markers like total hCG and inhibin-A, further improving screening performance^{(29, 35, 36)}. Studies utilizing ML algorithms, such as SVM and classification trees, have developed predictive models that outperform traditional statistical methods. By integrating data from both trimesters, ML models can analyze a wider range of biomarkers, thus enhancing prediction accuracy and addressing dataset imbalances, particularly for scarce Down syndrome cases through techniques like synthetic minority over-sampling. Research conducted by He et al.⁽¹⁸⁾ demonstrated that a random forest model improved second-trimester Down syndrome prediction, yielding an 85.2% detection rate in validation with external datasets. In the first trimester, Sun et al.⁽²⁷⁾ developed a personalized nomogram for trisomy 21 screening using fetal NT, and facial markers, attaining impressive AUC values of 0.983 and 0.979 for training and validation sets, respectively. Furthermore, Xu et al.⁽²⁵⁾ explored the efficacy of soft USM combined with NIPT, achieving notable sensitivity and specificity rates. Collectively, these studies highlight the promise of integrating ML techniques with biomarker data across trimesters to enhance Down syndrome screening and prediction. This approach ultimately improves detection rates up to 85% in the first trimester while minimizing false positives.

Discussion

Integrating ML with biomarker analysis for Down syndrome screening shows great promise in enhancing detection rates while minimizing false positives. The findings presented in Table 1 provide a comprehensive overview of the current studies exploring the integration of biomarkers and ML in the screening process for Down syndrome, highlighting both the advancements and the challenges faced in this evolving field. Recent studies by He et al.⁽¹⁸⁾ and Zhang et al.⁽²⁶⁾ demonstrate the effectiveness of large datasets and advanced algorithms, such as random forests and DL convolutional neural networks, in improving prediction models. By combining maternal serum markers, NT measurements, and ultrasonographic images with sophisticated ML techniques, detection rates have exceeded 85%. The ability of these models to generalize across diverse populations, supported by external validations, reinforces a robust approach to personalized prenatal care. Additionally, integrating NIPT with soft USM has achieved remarkable diagnostic sensitivity, specificity, and accuracy, highlighting the potential of multi-modal strategies for early identification of fetal anomalies⁽²⁵⁾. These advancements promise better clinical outcomes and suggest a shift toward individualized risk assessments for expectant mothers. Ongoing exploration of novel biomarkers and cutting-edge algorithms is expected to further enhance prenatal screening efficacy.

These findings emphasize the importance of ultrasound biomarkers and ML in improving prenatal diagnostics for conditions like Down syndrome. Results from Xu et al.⁽²⁵⁾ illustrate the effectiveness of combining soft USM with NIPT to boost detection rates of chromosomal abnormalities. Given the correlation between multiple unidentified subject matters and increased abnormality rates, establishing robust screening protocols that incorporate these biomarkers into clinical practice is essential. The high sensitivity (96.72%) and specificity (98.45%) of the combined diagnostic approach indicate a transformative shift in prenatal screening methodologies, providing reassurance to expectant parents and enabling informed decisions about further diagnostic interventions. Sun et al.’s⁽²⁷⁾ personalized nomogram highlights the value of tailored approaches in prenatal care, utilizing specific ultrasound parameters and advanced statistical techniques to create individualized screening tools. Strong AUC values and C-indices demonstrate its effectiveness in distinguishing between trisomy 21 and euploid pregnancies, aiding healthcare providers in delivering accurate risk assessments. Collectively, these studies suggest that combining ML with advanced imaging techniques enhances prenatal screening accuracy and aligns with the trend toward personalized medicine. Future research should focus on refining these algorithms, validating their effectiveness across diverse populations, and integrating them into routine clinical practice. Real-time data analytics and comprehensive training for healthcare professionals will be crucial for effective tool utilization, ultimately improving prenatal care outcomes for mothers and infants.

The integration of genetic and epigenetic biomarkers with advanced ML techniques represents a transformative approach to enhancing diagnostics for Down syndrome. Recent studies have clarified the complexity of Down syndrome through genetic alterations from trisomy 21 and significant epigenetic modifications affecting gene expression and regulation. Findings from Laufer et al.⁽³⁴⁾ work with low-pass WGBS highlight DNA methylation patterns as potential biomarkers for Down syndrome. The discovery of numerous differentially methylated regions associated with developmental and metabolic processes, particularly around neurodevelopment-critical genes, illustrates how epigenetic profiling can provide insights into the mechanisms underlying trisomy 21. The significant hypermethylation of the RUNX1 locus points to a targeted approach for understanding epigenomic dysregulation in Down syndrome. Such DMRs not only help explain individual variations in disease presentation but also connect these alterations to comorbid conditions like congenital heart disease. This comprehensive understanding is crucial for developing targeted therapeutic interventions and personalized care strategies. Volk et al.⁽³¹⁾ investigation of gene expression signatures in prenatal diagnostics further supports the potential of integrating transcriptomic analyses with ML. Their focus on DEGs among amniocyte samples demonstrates the feasibility of using comprehensive transcriptomic data to create predictive models for trisomy 21. The high AUC achieved with a multi-gene biomarker panel reinforces the viability of non-invasive prenatal screening. This combination of approaches marks a paradigm shift in diagnosing genetic disorders. By leveraging genetic and epigenetic markers alongside sophisticated computational methods, researchers have substantial potential to enhance diagnostic accuracy and timeliness. This integration not only facilitates early detection of Down syndrome but also deepens understanding of its pathophysiology, leading to improved management and outcomes for affected individuals. As researchers refine these methodologies and incorporate ML as a powerful analytical tool, the promise of accurate, early diagnosis of Down syndrome and related conditions becomes increasingly attainable, paving the way for enhanced clinical interventions and improved quality of life for individuals with Down syndrome.

The integration of ML techniques with genetic, epigenetic, and ultrasound biomarkers across trimesters marks a transformative step in the screening and prediction of Down syndrome, addressing previous challenges in detection rates and false positives. Advanced computational methods enable comprehensive analysis of complex datasets, enhancing the ability of predictive models to identify at-risk pregnancies. In the first trimester, established biomarkers such as NT and maternal serum markers achieve approximately 85%, detection rates, which are further improved in the second trimester with additional markers like total hCG and inhibin-A. ML algorithms, including SVM and random forest models, have shown superior performance compared to traditional statistical methods, demonstrating higher accuracy and efficiency in predictions⁽²⁷⁾. Notably, innovative approaches, such as personalized nomograms and the incorporation of soft USM with NIPT, have yielded impressive AUC values and sensitivity rates. Synthesizing data from both trimesters not only enhances prediction accuracy but also addresses dataset imbalances, particularly for rarer cases of Down syndrome. As these studies illustrate, the fusion of ML with biomarker analysis holds significant promise for advancing clinical practices, ultimately aiming to optimize screening processes and improve outcomes for expectant parents.

Clinical Implications

The integration of biomarkers and ML for predicting Down syndrome has significant clinical implications, particularly for enhancing prenatal screening protocols. Advanced algorithms and large datasets enable healthcare providers to achieve higher detection rates of trisomy 21 while keeping false positive rates low, thereby reducing unnecessary anxiety and invasive procedures for expectant parents. Combining maternal serum markers, NT measurements, soft USM, and NIPT allows for a personalized risk assessment tailored to individual patients. This approach not only improves diagnostic accuracy but also aids in informed decision-making regarding further diagnostic interventions and management strategies. As these methods evolve and become standard in clinical practice, they promise to transform prenatal care, resulting in better outcomes for mothers and infants through earlier detection and intervention for conditions linked to Down syndrome. The focus on personalized risk assessments, along with the high sensitivity and specificity of these approaches, reassures parents and empowers healthcare professionals to provide more precise and effective prenatal care, facilitating early interventions and improved management of associated conditions.

Study Limitations

Despite promising advancements in integrating ML with biomarker analysis for predicting Down syndrome, several limitations must be acknowledged. A major challenge is the dependence on large datasets that may not represent diverse populations, potentially leading to biases in model performance and generalizability. Variability in biomarkers among different groups can affect efficacy, making validation in diverse demographics essential. The complexity of genetic and epigenetic factors in Down syndrome further complicates risk prediction, necessitating thorough training and validation of ML algorithms to prevent overfitting and maintain reliability in clinical settings. Additionally, healthcare providers may encounter barriers to adopting these technologies due to limited resources, inadequate training, or challenges in integrating them into current workflows. Finally, ethical issues related to data privacy, informed consent, and the implications of prenatal screening results must be carefully managed to ensure patients feel secure and informed throughout the screening process.

Conclusion

Integrating ML with biomarker analysis for Down syndrome screening marks a significant advancement in prenatal diagnostics, resulting in improved detection rates and fewer false positives. By merging maternal serum markers, ultrasound measurements, and advanced algorithms, studies indicate the potential for personalized risk assessments and better clinical outcomes. The addition of genetic and epigenetic biomarkers enhances this approach, providing insights into trisomy 21 mechanisms and aiding in targeted interventions. The high sensitivity and specificity of these multi-modal strategies highlight their transformative effect on prenatal care, facilitating informed decision-making for expectant parents. As research continues to refine these methods and validate their effectiveness across diverse populations, the prospect of accurate and timely Down syndrome diagnosis becomes increasingly achievable, ultimately improving management and quality of life for affected individuals.