Application of an individualized nomogram in first-trimester screening for trisomy 21

Pearls and Pitfalls of First-Trimester US Screening and Prenatal Testing: A Pictorial Review

First-trimester US is imperative in evaluation of early pregnancy to confirm pregnancy location and number and gestational age. The 2024 Society of Radiologists in Ultrasound consensus conference established a first-trimester lexicon to highlight the importance of clear and concise language, which is incorporated and featured by the authors. With improved technologies and understanding of fetal development, first-trimester anatomic studies, between 11 weeks and 13 weeks 6 days gestation, are becoming more frequently used. While not a replacement for the second-trimester anatomic study, systematic evaluation of fetal anatomy at this early gestational age allows detection of 40%-70% of anomalies, whether structural or related to aneuploidy. All patients, regardless of age or baseline risk, should be offered screening and diagnostic testing for chromosomal abnormalities. A variety of prenatal screening approaches are available, each with strengths and limitations. Noninvasive prenatal testing with detection of fetal cell-free DNA can be performed in the first trimester and is the most sensitive and specific screening for the common fetal aneuploidies, but is not equivalent to diagnostic testing. Alternatively, serum analytes for maternal biomarkers in conjunction with nuchal translucency (NT) measurement can be used to calculate a risk estimate for common trisomies. Increased NT is the most common abnormality seen in the first trimester. Positive screening results, increased NT, or other anomaly at US should prompt genetic counseling and be confirmed with diagnostic testing (chorionic villus sampling or amniocentesis). Early detection of aneuploidy and structural anomalies allows counseling and informs decisions for pregnancy management. ©RSNA, 2025 Supplemental material is available for this article.

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing

BACKGROUND

Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI-IQA) system to audit images from the four key planes used in first-trimester scanning.

METHODS

The AI-IQA system was developed based on the YOLOv7 structure detection network and a multi-branch image quality regression network using a large multicenter internal dataset. Clinical validation was performed using 567 cases scanned by four radiologists with different experience levels, of which 349 were performed without AI-IQA feedback (clinical test set 1) and 218 were performed after 2-3 rounds of AI-IQA feedback (clinical test set 2). The proportion of standard images obtained and detailed expert audit results were compared to verify whether AI-IQA could objectively and accurately provide feedback on deficiencies in nonstandard images to assist radiologists at different experience levels in improving image quality.

RESULTS

In the internal test set, the AI-IQA system achieved high average accuracy precision, recall and F1-score in auditing the overall plane quality (0.881, 0.833, 0.842 and 0.837, respectively) and structure quality (0.906, 0.861, 0.857 and 0.859, respectively). In clinical test sets 1 and 2, AI-IQA results showed strong consistency with expert assessment results, with the average Cohen's Kappa coefficient exceeding 0.8 for all four planes. In addition, following AI-IQA feedback, the proportion of standard images obtained by junior and mid-level radiologists increased by 7.7% and 5.1%, respectively. AI-IQA takes only 0.05 s to assess each image, while experts require more than 20 s (p < 0.001).

CONCLUSIONS

The proposed AI-IQA system proved to be a highly accurate and efficient method of automatically auditing first-trimester scanning image quality, providing precise and rapid key plane quality control. This tool can also assist radiologists with different levels of experience to improve the image quality.

Advancements in machine learning and biomarker integration for prenatal Down syndrome screening

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.

A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database

Introduction

The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea.

Methods

We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression.

Results

A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well.

Discussion

Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

BACKGROUND

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

OBJECTIVE

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

METHODS

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

RESULTS

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

CONCLUSIONS

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.

A novel artificial intelligence model for fetal facial profile marker measurement during the first trimester

BACKGROUND

To study the validity of an artificial intelligence (AI) model for measuring fetal facial profile markers, and to evaluate the clinical value of the AI model for identifying fetal abnormalities during the first trimester.

METHODS

This retrospective study used two-dimensional mid-sagittal fetal profile images taken during singleton pregnancies at 11-13+ 6 weeks of gestation. We measured the facial profile markers, including inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial-maxillary angle (FMA), frontal space (FS) distance, and profile line (PL) distance using AI and manual measurements. Semantic segmentation and landmark localization were used to develop an AI model to measure the selected markers and evaluate the diagnostic value for fetal abnormalities. The consistency between AI and manual measurements was compared using intraclass correlation coefficients (ICC). The diagnostic value of facial markers measured using the AI model during fetal abnormality screening was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

A total of 2372 normal fetuses and 37 with abnormalities were observed, including 18 with trisomy 21, 7 with trisomy 18, and 12 with CLP. Among them, 1872 normal fetuses were used for AI model training and validation, and the remaining 500 normal fetuses and all fetuses with abnormalities were used for clinical testing. The ICCs (95%CI) of the IFA, MNM angle, FMA, FS distance, and PL distance between the AI and manual measurement for the 500 normal fetuses were 0.812 (0.780-0.840), 0.760 (0.720-0.795), 0.766 (0.727-0.800), 0.807 (0.775-0.836), and 0.798 (0.764-0.828), respectively. IFA clinically significantly identified trisomy 21 and trisomy 18, with areas under the ROC curve (AUC) of 0.686 (95%CI, 0.585-0.788) and 0.729 (95%CI, 0.621-0.837), respectively. FMA effectively predicted trisomy 18, with an AUC of 0.904 (95%CI, 0.842-0.966). MNM angle and FS distance exhibited good predictive value in CLP, with AUCs of 0.738 (95%CI, 0.573-0.902) and 0.677 (95%CI, 0.494-0.859), respectively.

CONCLUSIONS

The consistency of fetal facial profile marker measurements between the AI and manual measurement was good during the first trimester. The AI model is a convenient and effective tool for the early screen for fetal trisomy 21, trisomy 18, and CLP, which can be generalized to first-trimester scanning (FTS).

Reference values for ductus venosus Doppler velocity indices between 11 and 13+6 weeks of gestation: A single-center prospective study in Iran

BACKGROUND

This study aimed to investigate reference Doppler velocimetry indices (DVIs) of the fetal ductus venosus (DV) during 11-13 + 6 gestational weeks.

MATERIALS AND METHODS

In a prospective observation over referrals to a single tertiary care center in a 2-year interval, normal singleton pregnancies with fetal crown-rump lengths (CRLs) of 43-80 mm were examined by a single experienced sonographer for their DV pulsatility index (DVPI), DV resistance index (DVRI), and S-wave maximum velocity/A-wave minimum velocity (S/A ratio). Multinomial and quantile regression functions were used to analyze the effect of gestational age (estimated by CRL) on reference values (5th and 95th percentiles of the distribution in each gestational day/week). P < 0.05 was considered significant.

RESULTS

Over a sample of 415 participants with a mean/median gestational age of 12 + 1 weeks, no significant correlations were found between the CRL and DVIs using multinomial regression functions (linear model best fitted for all [DVPI: B coefficient = 0.001, P = 0.235] [DVRI: B coefficient = 0.001, P = 0.287] [DV S/A: B coefficient = 0.010, P = 283]). Quantile regression analyses of DVIs' reference values were nonsignificant across the CRL range except for the DVRI ([5th regression line: coefficient = -0.004, P = 0.018] [95th regression line: coefficient = -0.001, P = 0.030]).

CONCLUSION

Reference values for DVPI, DVRI, and DV S/A ratios were established as 0.80-1.39, 0.62-0.88, and 2.57-6.70, respectively. Future meta-analyses and multicenter studies are required to incorporate DV DVIs into an updated universal version of the practice.

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm

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.%.

Ultrasonographic study of fetal facial profile markers during the first trimester

Background

To establish reference ranges of fetal facial profile markers and study their correlations with crown-rump length (CRL) during the first trimester (11 ~ 13+ 6 weeks’ gestation) in a Chinese population.

Methods

Ultrasonographic images of measuring fetal nuchal translucency (NT) were retrospectively selected randomly in normal fetuses whose parents were both Chinese. The facial markers included inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial maxillary angle (FMA) and profile line (PL) distance. These markers were measured through ViewPoint 6 software by two experienced sonographers.

Results

Three hundred and eighty fetuses were selected. The ICCs (95 % CI) of intra-operator 1 reproducibility of IFA, MNM angle, FMA, PL distance were 0.944 (0.886 ~ 0.973), 0.804 (0.629 ~ 0.902), 0.834 (0.68 ~ 0.918) and 0.935 (0.868 ~ 0.969), respectively. The ICCs (95 % CI) of intra-operator 2 reproducibility of IFA, MNM angle, FMA, PL distance were 0.931 (0.857 ~ 0.967), 0.809 (0.637 ~ 0.904), 0.786 (0.600 ~ 0.892) and 0.906 (0.813 ~ 0.954), respectively. The ICCs (95 % CI) of inter-operator reproducibility of IFA, MNM angle, FMA, PL distance were 0.885 (0.663 ~ 0.953), 0.829 (0.672 ~ 0.915), 0.77 (0.511 ~ 0.891) and 0.844 (0.68 ~ 0.925), respectively. The average ± SD of IFA, MNM angle, FMA and PL distance were 80.2°±7.25°, 4.17°±1.19°, 75.36°±5.31°, 2.78 ± 0.54 mm, respectively. IFA and PL distance significantly decreased with CRL, while MNM angle and FMA significantly increased with CRL.

Conclusions

It was feasible to measure fetal facial markers during the first trimester. In Chinese population, the reference ranges of IFA, MNM angle, FMA and PL distance were 80.2°±7.25°, 4.17°±1.19°, 75.36°±5.31°, 2.78 ± 0.54 mm, respectively, and the measurements were found to correlate with CRL.

Second trimester maternal serum D-dimer combined with alpha-fetoprotein and free β-subunit of human chorionic gonadotropin predict hypertensive disorders of pregnancy: a systematic review and retrospective case-control study

Background

This study investigated whether maternal serum D-dimer (DD) alone or DD combined with alpha-fetoprotein (AFP) and free β-subunit of human chorionic gonadotropin (free β-hCG) in the second trimester could be used to predict hypertensive disorders of pregnancy (HDP).

Materials and methods

In this retrospective case–control study, the data of gravidas patients who delivered at hospital were divided into the following groups: control (n = 136), gestational hypertension (GH, n = 126), preeclampsia (PE, n = 53), and severe preeclampsia (SPE, n = 41). Receiver operator characteristic (ROC) curves were used to evaluate the diagnostic value of maternal serum DD, AFP, and free β-hCG levels for HDP.

Results

DD levels of the GH, PE, and SPE groups were significantly higher than that of the control group (P < 0.001). The order of effectiveness for models predicting HDP was as follows: DD + AFP + free β-hCG > DD > DD + AFP > DD + free β-hCG > AFP + free β-hCG > AFP > free β-hCG. For predicting different types of HDP, DD alone had the best diagnostic value for SPE, followed by PE and GH. DD alone had a sensitivity of 100% with a 0% false negative rate and had the highest positive likelihood ratio (+ LR) for SPE. DD alone in combination with AFP alone, free β-hCG alone and AFP + free β-hCG could reduce false positive rate and improve + LR.

Conclusion

DD is possible the best individual predictive marker for predicting HDP. Levels of DD alone in the second trimester were positively correlated with the progression of elevated blood pressure in the third trimester, demonstrating the predicting the occurrence of HDP. The risk calculation model constructed with DD + free β-hCG + AFP had the greatest diagnostic value for SPE.

A novel ultrasound-based approach to investigate extramedullary haematopoiesis in foetal spleen

Foetal spleen is described as a transient focus of haematopoiesis between the 3^rd and 5^th month of gestation: this function is however entirely replaced by the bone marrow before the end of pregnancy. This study identifies haematopoiesis in foetal spleen by exploring changes of echogenicity during its development throughout gestation. Two intervals of pregnancy were studied: Mid-Pregnancy (Mid-P, 19-23 weeks) and End-Pregnancy (End-P, 37-41 weeks). The foetal spleen was investigated in 80 pregnant women (41 vs 39). Due to quality criteria the comparison was made between 60 images (30 Mid-P vs 30 End-P). The acquisition of splenic parenchyma was followed by clustering segmentation. We identified two new parameters resulted from the clustering segmentation: Dark Ratio (DR) and Light Ratio (LR). These are related to splenic echogenicity expressing the percentage of dark and light signal in the clustered image, influenced by blood cellularity. The mean of DR value was different among the 2 groups (0.0631 vs 0.0483, P = 0.014), while LR did not show any significant differences. We conclude that DR may represent a reliable radiomic parameter in the determination of extramedullary haematopoiesis in the spleen.