Why are congenital heart defects being missed?

Fetal Cardiovascular Magnetic Resonance: History, Current Status, and Future Directions

Fetal cardiovascular magnetic resonance imaging (MRI) has emerged as a complementary modality for prenatal imaging in suspected congenital heart disease. Ongoing technical improvements extend the potential clinical value of fetal cardiovascular MRI. Ascertaining equivocal prenatal diagnostics obtained with ultrasonography allows for appropriate parental counseling and planning of postnatal surgery. This work summarizes current acquisition techniques and clinical applications of fetal cardiovascular MRI in the prenatal diagnosis and follow-up of fetuses with congenital heart disease. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

True umbilical cord knot detection via active scanning: a prospective study on accuracy and visualization factors

BACKGROUND

True umbilical cord knot (TUCK) is frequently missed in prenatal ultrasound (US), hindering standardized management and risk assessment of adverse perinatal outcomes. This study aimed to assess TUCK detection accuracy using active umbilical cord (UC) scanning and identify factors affecting prenatal visualization.

METHODS

A prospective study of 378 pregnant women (11-40 weeks) was conducted. Experienced and novice physicians sequentially scanned the full UC, grading umbilical cord ultrasonic image quality (UCUIQ) as sufficient (scale 1), restricted (scale 2), or poor (scale 3). Factors affecting UCUIQ were analyzed using multiple logistic regression, and diagnostic accuracy was evaluated. Cases for diagnosis were confirmed at delivery.

RESULTS

Interobserver agreement for UCUIQ grading was excellent (К = 0.979). Gestational week emerged as the primary factor influencing UC visualization (P < 0.05), with ultrasound achieving a diagnostic accuracy of no less than 89.3% for TUCK detection during the 17-26 weeks gestational period.

CONCLUSIONS

Gestational week significantly influenced TUCK detection, with high accuracy at 17-26 weeks. Active UC scanning during this period improved detection accuracy of TUCK.

Mitochondrial microRNAs (mitomiRs) as emerging biomarkers and therapeutic targets for chronic human diseases

Mitochondria are membrane-bound cell organelles that undertake the majority of the energetic and metabolic processes within the cell. They are also responsible for mediating multiple apoptotic pathways, balancing redox charges, and scavenging reactive oxygen species. MicroRNAs, which are short, non-coding RNAs widely known for regulating gene expression at the post-transcriptional level, regulate many of these processes. The specific microRNAs that directly or indirectly control mitochondrial dynamics are called mitochondrial miRNAs (mitomiRs). The broadest classification of this type of ncRNA encompasses nuclear-encoded miRNAs that interact with cytoplasmatic mRNAs associated with mitochondrial activity. At the same time, a more specific subset comprises nuclear-encoded miRNAs that translocate into the mitochondria to interact with mRNAs inside of this organelle. Finally, the smallest group of mitomiRs includes those codified by mtDNA and can regulate endogenous mitochondrial transcripts or be transported into the cytoplasm to modulate circulating mRNAs. Regardless of the origin or action mechanism, mitomiRs have been recently recognized to have a key role in the progression of a variety of chronic disorders, such as neurodegenerative and cardiovascular diseases, diabetes, asthma, depression, and even cancer. All of these progressive pathologies have been tightly linked to mitochondrial dysregulation. They are further associated with an aberrant expression of specific miRNAs that regulate cellular metabolism, positioning mitomiRs as reliable biomarkers for diagnosing several chronic diseases. These molecular indicators have also provided insights into how these conditions progress, allowing for the development of different miRNA-based treatment strategies that target dysregulated mitochondrial-related genes, reestablishing their baseline activity and restricting further disease progression.

High Heterogeneity in Prenatal Detection of Severe Congenital Heart Defects Among Physicians, Hospitals and Regions in Quebec

BACKGROUND

Prenatal detection rates (PDRs) of severe congenital heart defects (SCHDs) are often presented as regional and national aggregates, which might hide significant heterogeneity in PDRs among physicians, hospitals, and regions. The objective was to quantify the variability in the sensitivity of second-trimester ultrasound examination (U/S) to detect SCHDs, and to identify at which level this variability was the greatest.

METHODS

This was a retrospective observational cohort of all pregnancy-child dyads with SCHDs in Quebec between 2007 and 2015. We matched the clinical data from the hospitals with the administrative data from the health care system. The variability at each level was estimated using multilevel models by calculating intraclass correlation coefficients.

RESULTS

Of 1274 SCHD, 697 were diagnosed prenatally following a referral for a suspected cardiac anomaly on U/S, yielding a sensitivity of 54.7% (95% confidence interval [CI], 52.0%-57.4%). Significant heterogeneity was observed among physicians, hospitals, and regions with the greatest heterogeneity among physicians. The U/S sensitivities in the lowest quartile for physicians, hospitals, and regions were 27.4%, 29.0%, and 39.8%, and those in the highest quartile were 87.3%, 70.1%, and 62.9%, respectively. The mean difference of sensitivity between the lowest and highest quartiles was 59.9% (95% CI, 51.7-68.1) for physicians, and 41.1% (95% CI, 30.3-51.9) for hospitals. The intraclass correlation coefficients at the physician level indicated the greatest heterogeneity among physicians (intrahospital).

CONCLUSIONS

There was considerable heterogeneity in PDRs between physicians and hospitals. The driver of the heterogeneity seemed to be at the physician level, with higher interphysician variability. Any measures of improvement should be directed to the physician level.

The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review

Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an array of clinical applications, including image quality control, cardiac function measurements, defect detection, and classifications. Collectively, we answer how integrating AI technologies and echocardiography can help improve the detection of congenital heart defects. Particularly, the superior sensitivity of AI-based congenital heart defect (CHD) detection in the fetus (>90%) allows it to be potentially translated into the clinical workflow as an effective screening tool in an obstetric setting. However, the current AI technologies still have many limitations, and more technological developments are required to enable these AI technologies to reach their full potential. Also, integrating diagnostic AI technologies into the clinical workflow should resolve ethical concerns. Otherwise, deploying diagnostic AI may not address low-resource populations' healthcare access disadvantages. Instead, it will further exacerbate the access disparities. We envision that, through the combination of tele-echocardiography and AI, low-resource medical facilities may gain access to the effective detection of CHD at the prenatal stage.

Second trimester echocardiography

Fetal echocardiography involves a comprehensive cardiac assessment aiming to make a complete structural examination of the heart as well as to detect signs of cardiovascular adaptation to different insults. For the former, this assessment entails expert's evaluation of the anatomy of the heart including additional views beyond the five axial views used in cardiac screening examinations and always complemented with colour and pulsed Doppler. Echocardiography may accurately diagnose most congenital heart defects in fetal life, which enables adjusting the perinatal management. For the latter, echocardiography encompasses cardiac morphometric assessment to identify signs of cardiac remodeling indicative of cardiac adaptation in structure, shape, and size in response to underlying diseases, and cardiac functional assessment to detect signs of systolic and/or diastolic dysfunction. The most used parameters to study the systolic function (stroke volume, cardiac output, ejection fraction, fractional shortening, and mitral and tricuspid annular plane systolic excursion), diastolic function (characteristics of flow in the precordial veins and through the atrioventricular valves) and global myocardial function (myocardial performance index) will be discussed in this review.

A comprehensive scoping review on machine learning-based fetal echocardiography analysis

Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the automation of fetal echocardiographic analysis; this review presents the findings from a literature review in this area. Searches were queried at leading indexing platforms ACM, IEEE Xplore, PubMed, Scopus, and Web of Science, including papers published until July 2023. In total, 343 papers were found, where 48 papers were selected to compose the detailed review. The reviewed literature presents research on neural network-based methods to identify fetal heart anatomy in classification and segmentation modelling. The reviewed literature uses five categorical technical analysis terms: attention and saliency, coarse to fine, dilated convolution, generative adversarial networks, and spatio-temporal. This review offers a technical overview for those already working in the field and an introduction to those new to the topic.

Assessing Environmental Justice in Mexico: How Polluting Industries and Healthcare Disparities Impact Congenital Heart Defects

BACKGROUND

Congenital heart defects (CHDs) are the most prevalent birth defects globally and the second leading cause of death in Mexican children under five. This study examines how industrial activity and social vulnerabilities independently and jointly influence CHD incidence across 2446 Mexican municipalities from 2008 to 2019.

METHODS

Using negative binomial regression models, we evaluated associations between polluting industries, healthcare access, and CHD incidence. We analyzed these factors independently, jointly, and through interaction terms to assess potential effect modification by healthcare access. Incidence rate ratios (IRRs) and 95% confidence intervals were estimated across healthcare access strata.

RESULTS

Municipalities without healthcare facilities were more likely to host polluting industries, highlighting structural inequities. The presence of polluting industries was associated with increased CHD incidence, even after adjusting for healthcare access. For instance, municipalities with poor healthcare access and two or more polluting industries exhibited a 42% higher CHD incidence (IRR = 1.42, 95% CI: 1.25-1.60) compared to a 26% increase in municipalities with better healthcare access (IRR = 1.26, 95% CI: 1.02-1.57).

CONCLUSIONS

These results show how environmental pollutant exposure and social vulnerabilities interact synergistically, disproportionately impacting socially vulnerable populations. Targeted policy interventions addressing both environmental pollution and healthcare inequities are crucial. Further research is also needed to clarify the mechanisms linking pollution to CHDs and to guide public health strategies aimed at reducing these disparities in Mexico.

Initiating a Fetal Cardiac Program from Scratch in Low- and Middle-Income Countries: Structure, Challenges, and Hopes for Solutions

Although fetal cardiac programs are well established in developed countries, establishing an efficient program in low- and middle-income countries (LMICs) is still considered a significant challenge. Substantial obstacles usually face the initiation of fetal cardiac service from scratch in LMICs. The primary structural frame of a successful fetal cardiac program is described in detail, emphasizing the required team members. The potential challenges for starting fetal cardiac services in LMICs include financial, awareness-related, prenatal obstetric screening, sociocultural, psychosocial, and social support factors. These challenges could be solved by addressing these barriers, such as collecting funds for financial support, raising awareness among families and health care providers, telemedicine, building international health partnerships, modifying training protocols for fetal cardiologists and sonographers, and initiating support groups and social services for families with confirmed fetal cardiac disease. Initiating a successful fetal cardiac program requires multi-aspect structural planning. The challenges for program initiation require diverse efforts, from modified training and promoting awareness of care providers and the community to governmental and nonprofit organizations' collaborations for proper building and utilization of program resources.

Risk factors for isolated congenital heart defects in infants from Western Mexico

Congenital heart defects (CHDs) are caused by a complex interaction between numerous genetic and environmental risk factors, some of which may differ between different populations. A case-control study was conducted among 1232 newborns, including 308 patients with isolated CHDs (cases) and 924 infants without birth defects (controls), born all during the period 2009-2023 at the Hospital Civil de Guadalajara "Dr. Juan I. Menchaca" (Guadalajara, Mexico). Potential parental risk factors for CHDs were compared using multivariate logistic regression analysis to evaluate the deviance explained by different variables of interest. Consanguinity [adjusted odds ratio (aOR) = 3.3; 95% confidence interval (CI) 1.3-8.5], relatives with CHD (aOR = 8.5; 95% CI 5.3-13.8), maternal first-trimester exposure to diabetes (aOR = 3.5; 95% CI 2.4-5.1), hypertension (aOR = 2.6; 95% CI 1.5-4.4), alcohol consumption (aOR = 1.5; 95% CI 1.0-2.1), and illicit drug use (aOR = 2.4; 95% CI 1.2-5.3), as well as for the paternal history of alcohol consumption (aOR = 1.4; 95% CI 1.0-1.8) and illicit drug use (aOR = 2.7; 95% CI 1.7-4.1), were associated with CHDs. Contrarily, aOR for maternal age ≤19 years (aOR = 0.6; 95% CI 0.4-0.8) and maternal first-trimester coffee consumption (aOR = 0.7; 95% CI 0.5-0.9) have protective odds. Our results suggest that genetic factors, maternal diseases, environmental exposures, and reproductive factors can increase the occurrence of isolated CHDs in our sample, and they are discussed as clues in its pathogenesis.

Factors affecting the accuracy of fetal cardiac ultrasound screening in the first trimester of pregnancy

PURPOSE

Most studies on the performance of first-trimester cardiac screening have concentrated on comparing the detection rate between different protocols and not on the actual reason for false-negative results. Herein, we report the performance of first-trimester congenital heart disease (CHD) screening and factors that may affect the detection rate of CHDs.

METHODS

This retrospective observational study included patients who underwent first-trimester screening and subsequently gave birth at our facility. We analyzed the performance of first-trimester screening for CHD and major CHD (CHD requiring cardiac surgery or interventional catheterization within 12 months of birth).

RESULTS

Of the 6614 fetuses included, 53 had CHD and 35 had major CHD. For the prenatal diagnosis of CHD, the detection rate, specificity, positive predictive value, negative predictive value, and first-trimester detection rate for CHD were 64.1%, 99.9%, 94.4%, 99.7%, and 82.9%, respectively; the respective values for major CHD were 85.7%, 99.96%, 93.75%, 99.92%, and 85.7%. The detection rate was not significantly different when classified by crown-rump length or number of fetuses. A weak correlation was observed between low detection rate of major CHD and lower maternal body mass index (BMI) (correlation ratio: 0.17). The detection rate was significantly higher when the fetus was scanned with its spine at the 5-7 o'clock position (posterior spine) than at other positions (odds ratio: 3.82, 95% confidence interval: 1.16-12.5, p = 0.02).

CONCLUSION

Posterior spine contributes to an improved diagnostic rate in first-trimester CHD screening. In addition, sonographers must recognize that low maternal BMI is a risk factor of false-negative results.

Study on ultrasound diagnosis and pathological anatomy of fetal complex congenital heart disease in the first trimester

PURPOSE

To assess the feasibility of using the stereo-microscope to identify the pathological anatomy of the congenital heart diseases in the first trimester.

METHODS

Fifteen fetuses of 8-12 weeks aborted due to prevent miscarriage failure and 42 fetuses of 11-14 weeks with congenital heart diseases were included in the study, we dissected their hearts through a stereo-microscope, then compared with the prenatal ultrasonographic diagnosis.

RESULTS

Using stereomicroscopy, the positive view of the heart and the great arteries, the long axis view of the aortic arch, the inflow tract view of the bottom heart, the semilunar valve view of the bottom heart, and the transverse section of the ventricle were showed contented and obtained satisfactory images, but the structure of atrioventricular valve and venous system had a lower rate of display.

CONCLUSION

The characteristic pathological changes of cardiac inflow and outflow tract can be obtained by dissecting the heart sequential under the stereo-microscopy. However it is often difficult to obtain satisfactory pathological sections for pulmonary venous abnormalities and Ebstein anomaly.

Artificial Intelligence in Fetal and Pediatric Echocardiography

Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI's current applications, challenges, and future directions in fetal and pediatric echocardiography.

Automated Assessment of the Pulmonary Artery-to-Ascending Aorta Ratio in Fetal Cardiac Ultrasound Screening Using Artificial Intelligence

The three-vessel view (3VV) is a standardized transverse scanning plane used in fetal cardiac ultrasound screening to measure the absolute and relative diameters of the pulmonary artery (PA), ascending aorta (Ao), and superior vena cava, as required. The PA/Ao ratio is used to support the diagnosis of congenital heart disease (CHD). However, vascular diameters are measured manually by examiners, which causes intra- and interobserver variability in clinical practice. In the present study, we aimed to develop an artificial intelligence-based method for the standardized and quantitative evaluation of 3VV. In total, 315 cases and 20 examiners were included in this study. We used the object-detection software YOLOv7 for the automated extraction of 3VV images and compared three segmentation algorithms: DeepLabv3+, UNet3+, and SegFormer. Using the PA/Ao ratios based on vascular segmentation, YOLOv7 plus UNet3+ yielded the most appropriate classification for normal fetuses and those with CHD. Furthermore, YOLOv7 plus UNet3+ achieved an arithmetic mean value of 0.883 for the area under the receiver operating characteristic curve, which was higher than 0.749 for residents and 0.808 for fellows. Our automated method may support unskilled examiners in performing quantitative and objective assessments of 3VV images during fetal cardiac ultrasound screening.

Learning Curve of First-Trimester Detailed Cardiovascular Ultrasound Screening by Moderately Experienced Obstetricians in 3509 Consecutive Unselected Pregnancies with Fetal Follow-Up

Our primary objective was to assess the effectiveness of detailed cardiovascular ultrasound screening during the first trimester, which was performed by obstetricians with intermediate experience. We collected first-trimester fetal cardiac screening data from an unselected pregnant population at RMC-Fetal Medicine Center during a study period spanning from 1 January 2010, to 31 January 2015, in order to analyze our learning curve. A pediatric cardiologist performed a follow-up assessment in cases where the examining obstetrician determined that the fetal cardiac screening results were abnormal or high-risk. Overall, 42 (0.88%) congenital heart abnormalities were discovered prenatally out of 4769 fetuses from 4602 pregnant women who had at least one first-trimester cardiac ultrasonography screening. In total, 89.2% of the major congenital heart abnormalities (27 of 28) in the following fetuses were discovered (or at least highly suspected) at the first-trimester screening and subsequent fetal echocardiography by the pediatric cardiology specialist. Of these, 96.4% were diagnosed prenatally. According to our results, the effectiveness of first-trimester fetal cardiovascular ultrasound screening conducted by moderately experienced obstetricians in an unselected ('routine') pregnant population may reach as high as 90% in terms of major congenital heart defects, provided that equipment, quality assurance, and motivation are appropriate.

Fetal Tele-Echocardiography-An Approach to Improving Diagnosis and Management

Introduction: Antenatal diagnosis of cardiac abnormalities and counselling parents about postnatal care require a multidisciplinary team, which includes a paediatric cardiologist, a neonatologist, and a fetal medicine physician. Some of these kinds of expertise are not available in all centres with fetal medicine expertise. However, with modern technology, this could be provided remotely. Our objective was to assess the feasibility and outcomes of prenatal multidisciplinary tele-echocardiography diagnostic and counselling services. Materials and Methods: Two centres based in separate countries provided a joint diagnostic and counselling service over a period of 14 months. The primary centre performed the fetal echocardiography with a Voluson E10 machine, and images were transmitted live using Zoom OPS system with video-consultation and counselling. The fetal echo was performed using the ISUOG Guidelines check list. Results: There was an initial feasibility period of 2 months during which 10 women whose fetuses had normal hearts were scanned to test the workability of the system. Over a period of 12 months, 513 high-risk fetuses were then scanned, and out of these, 27 had congenital malformations. The most common were hypoplastic left heart syndrome (HHLS) and atrio-ventricular septal defect. Tele-echocardiography and counselling were successful in all the cases. Satisfaction with the service was 3.8/4, with the main limitation being the need for further referral to a tertiary centre for delivery. Conclusions: Tele-echocardiography is reliable, and when combined with live counselling and support from a paediatric cardiologist, it is an option acceptable to patients. The greatest benefit was from being counselled by a team of experts at a single consultation rather than having to travel to another centre for consultation. With rapidly evolving technology, making video transmission easier and less expensive, we feel that consideration should be given not only to the development of tele-echocardiography but also to extending it to other aspects of fetal medicine.

Detection of congenital heart disease by neonatologist performed cardiac ultrasound in preterm infants

OBJECTIVE

We aimed to assess the frequency of de novo congenital heart disease (CHD) detection via neonatologist-performed cardiac ultrasounds (NPCU) in premature infants born at <30 weeks of gestation.

STUDY DESIGN

In this cross-sectional study (2004-2023) clinicians completing NPCU flagged de novo suspected CHD. All flagged NPCUs were cross-checked with cardiologists to confirm CHD diagnosis.

RESULTS

There were 2088 out of 3739 infants (56%) with at least one NPCU; 294 (14%) with cardiology referral. CHD diagnosis was confirmed in 109 of the 2088 (5.2%) infants. All major and critical CHD on NPCU imaging were suspected during NPCU and had prompt referral to the cardiology department.

CONCLUSION

De novo presentation of significant CHD continues to occur in the preterm population, emphasizing the need for recognizing CHD during NPCU. Optimizing NPCU training may benefit patients with early cardiology referral and review.

Assessment of NanoString technology as a tool for profiling circulating miRNA in maternal blood during pregnancy

Aim

Circulating maternal MicroRNA (miRNA) is a promising source of biomarkers for antenatal diagnostics. NanoString nCounter is a popular global screening tool due to its simplicity and ease of use, but there is a lack of standardisation in analysis methods. We examined the effect of user-defined variables upon reported changes in maternal blood miRNA during pregnancy.

Methods

Total RNA was prepared from the maternal blood of pregnant and control rats. miRNA expression was profiled using Nanostring nCounter. Raw count data were processed using nSolver using different combinations of normalisation and background correction methods as well as various background thresholds. A panel of 14 candidates in which changes were supported by multiple analysis workflows was selected for validation by RT-qPCR. We then reverse-engineered the nSolver analysis to gain further insight.

Results

Thirty-one putative differentially expressed miRNAs were identified by nSolver. However, each analysis workflow produced a different set of reported biomarkers and none of them was common to all analysis methods. Four miRNAs with known roles in pregnancy (miR-183, miR-196c, miR-431, miR-450a) were validated. No single nSolver analysis workflow could successfully identify all four validated changes. Reverse engineering revealed errors in nSolver data processing which compound the inherent problems associated with background correction and normalisation.

Conclusion

Our results suggest that user-defined variables greatly influence the output of the assay. This highlights the need for standardised nSolver data analysis methods and detailed reporting of these methods. We suggest that investigators in the future should not rely on a single analysis method to identify changes and should always validate screening results.

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions. In this study, we propose an automated method for segmenting the pulmonary artery, ascending aorta, and superior vena cava in the 3VV using a novel deep learning network named CoFi-Net. Our network incorporates a coarse-fine collaborative strategy with two parallel branches dedicated to simultaneous global localization and fine segmentation of the vessels. The coarse branch employs a partial decoder to leverage high-level semantic features, enabling global localization of objects and suppression of irrelevant structures. The fine branch utilizes attention-parameterized skip connections to improve feature representations and improve boundary information. The outputs of the two branches are fused to generate accurate vessel segmentations. Extensive experiments conducted on a collected dataset demonstrate the superiority of CoFi-Net compared to state-of-the-art segmentation models for 3VV segmentation, indicating its great potential for enhancing CHD diagnostic efficiency in clinical practice. Furthermore, CoFi-Net outperforms other deep learning models in breast lesion segmentation on a public breast ultrasound dataset, despite not being specifically designed for this task, demonstrating its potential and robustness for various segmentation tasks.

Controlled prospective study on ultrasound simulation training in fetal echocardiography: FESIM II

PURPOSE

To analyze the learning curves of ultrasound novices in fetal echocardiography during structured simulation-based ultrasound training (SIM-UT) including a virtual, randomly moving fetus.

METHODS

11 medical students with minimal (< 10 h) prior obstetric ultrasound experience underwent 12 h of structured fetal echocardiography SIM-UT in individual hands-on sessions during a 6-week training program. Their learning progress was assessed with standardized tests after 2, 4, and 6 weeks of SIM-UT. Participants were asked to obtain 11 fetal echocardiography standard planes (in accordance with ISUOG and AHA guidelines) as quickly as possible. All tests were carried out under real life, examination-like conditions on a healthy, randomly moving fetus. Subsequently, we analyzed the rate of correctly obtained images and the total time to completion (TTC). As reference groups, 10 Ob/Gyn physicians (median of 750 previously performed Ob/Gyn scans) and 10 fetal echocardiography experts (median of 15,000 previously performed Ob/Gyn scans) were examined with the same standardized tests.

RESULTS

The students showed a consistent and steady improvement of their ultrasound performance during the training program. After 2 weeks, they were able to obtain > 95% of the standard planes correctly. After 6 weeks, they were significantly faster than the physician group (p < 0.001) and no longer significantly slower than the expert group (p = 0.944).

CONCLUSION

SIM-UT is highly effective to learn fetal echocardiography. Regarding the acquisition of the AHA/ISUOG fetal echocardiography standard planes, the students were able to reach the same skill level as the expert group within 6 weeks.

Prenatal detection rates for congenital heart disease using abnormal obstetrical screening ultrasound alone as indication for fetal echocardiography

OBJECTIVE

To determine the live born prenatal detection rate of significant congenital heart disease (CHD) in a large, integrated, multi-center community-based health system using a strategy of referral only of patients with significant cardiac abnormalities on obstetrical screening ultrasound for fetal echocardiography. Detection rates were assessed for screening in both radiology and maternal fetal medicine (MFM). The impact on fetal echocardiography utilization was also assessed.

METHODS

This was a retrospective cohort study using an electronic health record, outside claims databases and chart review to determine all live births between 2016 and 2020 with postnatally confirmed sCHD that were prenatally detectable and resulted in cardiac surgery, intervention, or death within 1 year.

RESULTS

There were 214,486 pregnancies resulting in live births. Prenatally detectable significant CHD was confirmed in 294 infants. Of those 183 were detected for an overall live-born detection rate of 62%. Detection rates in MFM were 75% and in radiology were 52%. The number of fetal echocardiograms needed to detect (NND) sCHD was 7.

CONCLUSIONS

A focus on quality and standardization of obstetrical screening ultrasound with referral to fetal echocardiography for cardiac abnormalities alone achieves benchmark targets for live-born detection of significant CHD requiring fewer fetal echocardiograms.

Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2

BACKGROUND

Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images.

METHODS

A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience.

RESULTS

The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers.

CONCLUSION

We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

BACKGROUND

Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis.

METHOD

In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels-the pulmonary artery, aorta, and superior vena cava-in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images.

RESULTS

We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively.

CONCLUSIONS

Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

AI supported fetal echocardiography with quality assessment

This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18-22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals. A total of 5363 images from 2551 pregnancies were used for training and validation. The model's segmentation accuracy depended on image quality measured by a quality score (QS). It achieved an overall average accuracy of 0.91 (SD 0.09) across the test set, with images having above-average QS scoring 0.97 (SD 0.03). During prospective validation of 192 images, clinicians rated 44.8% (SD 9.8) of images as equal in quality, 18.69% (SD 5.7) favoring auto-captured images and 36.51% (SD 9.0) preferring manually captured ones. Images with above average QS showed better agreement on segmentations (p < 0.001) and QS (p < 0.001) with fetal medicine experts. Auto-capture saved additional planes beyond protocol requirements, resulting in more comprehensive echocardiographies. Low QS had adverse effect on both model performance and clinician's agreement with model feedback. The findings highlight the importance of developing and evaluating AI models based on 'noisy' real-life data rather than pursuing the highest accuracy possible with retrospective academic-grade data.

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection

OBJECTIVES

Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. A secondary aim was to compare initial screening diagnosis, which made use of live imaging at the point-of-care, with diagnosis by clinicians evaluating only stored images.

METHODS

All pregnancies with isolated severe CHD in the Northwestern region of The Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region and time period. We compared the accuracy of the initial clinical diagnosis (made in real time with access to live imaging) with that of the model (which had only stored imaging available) and with the performance of three blinded human experts who had access only to the stored images (like the model). We analyzed performance according to ultrasound study characteristics, such as duration and quality (scored independently by investigators), number of stored images and availability of screening views.

RESULTS

A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity, 53%). Model sensitivity and specificity were 91% and 78%, respectively. Blinded human experts (n = 3) achieved mean ± SD sensitivity and specificity of 55 ± 10% (range, 47-67%) and 71 ± 13% (range, 57-83%), respectively. There was a statistically significant difference in model correctness according to expert-graded image quality (P = 0.03). The abnormal cases included 19 lesions that the model had not encountered during its training; the model's performance in these cases (16/19 correct) was not statistically significantly different from that for previously encountered lesions (P = 0.41).

CONCLUSIONS

A previously trained DL algorithm had higher sensitivity than initial clinical assessment in detecting CHD in a cohort in which over 50% of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions to which it had not been exposed previously. Furthermore, when both the model and blinded human experts had access to only stored images and not the full range of images available to a clinician during a live scan, the model outperformed the human experts. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

The comprehensive study on the role of POSTN in fetal congenital heart disease and clinical applications

BACKGROUND

Congenital heart defect (CHD) is the most common congenital abnormality, and it has long been a clinical and public health concern. Our previous findings have found Periostin (POSTN) and Pappalysin-1 (PAPPA) as potential biomarkers for fetal CHD. We aim to further elucidate POSTN's role in fetal heart development and explore the clinical applicability of POSTN and PAPPA as diagnostic marker for fetal CHD. This study is poised to establish a theoretical framework for mitigating the incidence of CHD and advance a novel approach for prenatal screening of fetal CHD.

METHODS

We verified differential expression of POSTN and PAPPA in gravida serum and fetal amniotic fluid based on our previous research. We established the Postn knockout mouse by CRISPR/Cas9 to investigate whether Postn deletion leads to cardiac abnormalities in mice. Besides, we explored the mechanism of POSTN on heart development through Postn knockout mouse model and cell experiments. Finally, we established the logistic regression model and decision curve analysis to evaluate the clinical utility of POSTN and PAPPA in fetal CHD.

RESULTS

We observed a significant decrease in POSTN and increase in PAPPA in the CHD group. Atrial septal defects occurred in Postn-/- and Postn± C57BL/6 fetal heart, while ventricular septal defects with aortic saddle were observed in Postn± C57BL/6 fetal heart. Disruption of the extracellular matrix (ECM) in cardiomyocytes and multiple abnormalities in cellular sub-organelles were observed in Postn knockout mice. POSTN may positively regulate cell behaviors and unsettle ECM via the TGFβ-Smad2/3 signaling pathway. The combination of serum biomarkers POSTN and PAPPA with Echocardiogram can enhance the diagnostic accuracy of CHD. Furthermore, the comprehensive model including POSTN, PAPPA, and two clinical indicators (NT and age) exhibits significantly higher predictive ability than the diagnosis group without the use of serum biomarkers or clinical indicators.

CONCLUSIONS

It is the first evidence that Postn deletion leads to cardiac developmental abnormalities in fetal mice. This may involve the regulation of the TGFβ signaling pathway. Importantly, POSTN and PAPPA possess clinical utility as noninvasive prenatal promising screening indicators of CHD.

The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot

Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.

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.

Prevalence and detection rate of major congenital heart disease in twin pregnancies in Denmark

OBJECTIVE

To investigate the national prevalence and prenatal detection rate (DR) of major congenital heart disease (mCHD) in twin pregnancies without twin-to-twin transfusion syndrome (TTTS)-associated CHD in a Danish population following a standardized prenatal screening program.

METHODS

This was a national registry-based study of data collected prospectively over a 10-year period. In Denmark, all women with a twin pregnancy are offered standardized screening and surveillance programs in addition to first- and second-trimester screening for aneuploidies and malformation, respectively: monochorionic (MC) twins every 2 weeks from gestational week 15 and dichorionic (DC) twins every 4 weeks from week 18. The data were retrieved from the Danish Fetal Medicine Database and included all twin pregnancies from 2009-2018, in which at least one fetus had a pre- and/or postnatal mCHD diagnosis. mCHD was defined as CHD requiring surgery within the first year of life, excluding ventricular septal defects. All pregnancy data were pre- and postnatally validated in the local patient files at the four tertiary centers covering the entire country.

RESULTS

A total of 60 cases from 59 twin pregnancies were included. The prevalence of mCHD was 4.6 (95% CI, 3.5-6.0) per 1000 twin pregnancies (1.9 (95% CI, 1.3-2.5) per 1000 live births). The prevalences for DC and MC were 3.6 (95% CI, 2.6-5.0) and 9.2 (95% CI, 5.8-13.7) per 1000 twin pregnancies, respectively. The national prenatal DR of mCHD in twin pregnancies for the entire period was 68.3%. The highest DRs were in cases with univentricular hearts (100%) and the lowest with aortopulmonary window, total anomalous pulmonary venous return, Ebstein's anomaly, aortic valve stenosis and coarctation of the aorta (0-25%). Mothers of children with prenatally undetected mCHD had a significantly higher body mass index (BMI) compared to mothers of children with a prenatally detected mCHD (median, 27 kg/m2 and 23 kg/m2 , respectively; P = 0.02).

CONCLUSIONS

The prevalence of mCHD in twins was 4.6 per 1000 pregnancies and was higher in MC than DC pregnancies. The prenatal DR of mCHD in twin pregnancies was 68.3%. Maternal BMI was higher in cases of prenatally undetected mCHD. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning

OBJECTIVES

Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenatal diagnosis.

METHODS

Normal and abnormal fetal ultrasound heart images, including five standard views, were collected according to the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) Practice guidelines. You Only Look Once version 5 (YOLOv5) models were trained and tested. An excellent model was screened out after comparing YOLOv5 with other classic detection methods.

RESULTS

On the training set, YOLOv5n performed slightly better than the others. On the validation set, YOLOv5n attained the highest overall accuracy (90.67 %). On the CHD test set, YOLOv5n, which only needed 0.007 s to recognize each image, had the highest overall accuracy (82.93 %), and YOLOv5l achieved the best accuracy on the abnormal dataset (71.93 %). On the VSD test set, YOLOv5l had the best performance, with a 92.79 % overall accuracy rate and 92.59 % accuracy on the abnormal dataset. The YOLOv5 models achieved better performance than the Fast region-based convolutional neural network (RCNN) & ResNet50 model and the Fast RCNN & MobileNetv2 model on the CHD test set (p<0.05) and VSD test set (p<0.01).

CONCLUSIONS

YOLOv5 models are able to accurately distinguish normal and abnormal fetal heart ultrasound images, especially with respect to the identification of VSD, which have the potential to assist ultrasound in prenatal diagnosis.

Semi-Automatic Measurement of Fetal Cardiac Axis in Fetuses with Congenital Heart Disease (CHD) with Fetal Intelligent Navigation Echocardiography (FINE)

Congenital heart disease (CHD) is one of the most common organ-specific birth defects and a major cause of infant morbidity and mortality. Despite ultrasound screening guidelines, the detection rate of CHD is limited. Fetal intelligent navigation echocardiography (FINE) has been introduced to extract reference planes and cardiac axis from cardiac spatiotemporal image correlation (STIC) volume datasets. This study analyses the cardiac axis in fetuses affected by CHD/thoracic masses (n = 545) compared to healthy fetuses (n = 1543) generated by FINE. After marking seven anatomical structures, the FINE software generated semi-automatically nine echocardiography standard planes and calculated the cardiac axis. Our study reveals that depending on the type of CHD, the cardiac axis varies. In approximately 86% (471 of 542 volumes) of our pathological cases, an abnormal cardiac axis (normal median = 40-45°) was detectable. Significant differences between the fetal axis of the normal heart versus CHD were detected in HLHS, pulmonary atresia, TOF (p-value < 0.0001), RAA, situs ambiguus (p-value = 0.0001-0.001) and absent pulmonary valve syndrome, DORV, thoracic masses (p-value = 0.001-0.01). This analysis confirms that in fetuses with CHD, the cardiac axis can significantly deviate from the normal range. FINE appears to be a valuable tool to identify cardiac defects.

Evaluation of an artificial intelligent algorithm (Heartassist™) to automatically assess the quality of second trimester cardiac views: a prospective study

OBJECTIVES

The aim of this study was to evaluate the agreement between visual and automatic methods in assessing the adequacy of fetal cardiac views obtained during second trimester ultrasonographic examination.

METHODS

In a prospective observational study frames of the four-chamber view left and right outflow tracts, and three-vessel trachea view were obtained from 120 consecutive singleton low-risk women undergoing second trimester ultrasound at 19-23 weeks of gestation. For each frame, the quality assessment was performed by an expert sonographer and by an artificial intelligence software (Heartassist™). The Cohen's κ coefficient was used to evaluate the agreement rates between both techniques.

RESULTS

The number and percentage of images considered adequate visually by the expert or with Heartassist™ were similar with a percentage >87 % for all the cardiac views considered. The Cohen's κ coefficient values were for the four-chamber view 0.827 (95 % CI 0.662-0.992), 0.814 (95 % CI 0.638-0.990) for left ventricle outflow tract, 0.838 (95 % CI 0.683-0.992) and three vessel trachea view 0.866 (95 % CI 0.717-0.999), indicating a good agreement between the two techniques.

CONCLUSIONS

Heartassist™ allows to obtain the automatic evaluation of fetal cardiac views, reached the same accuracy of expert visual assessment and has the potential to be applied in the evaluation of fetal heart during second trimester ultrasonographic screening of fetal anomalies.

The Left Outflow Tract in Fetal Cardiac Screening Examination: Introduction of Quality Criteria Is Not Always Associated With an Improvement of Practice When Supervised by Humans

OBJECTIVES

Since 2016, the French CNEOF included the left ventricular outflow tract (LVOT) in the second and third trimester of pregnancy in addition to the four-chamber view and the parasagittal view of the right outflow tract. The objective of this study was to define quality criteria for fetal LVOT assessment and to perform a human audit of past and current practices, before and after the implementation of those quality criteria at a large scale.

METHODS

Seven quality criteria were investigated and rated from 0 to 1 during three periods of interest. Files were randomly selected from three centers, and average total and specific scores were calculated.

RESULTS

LVOT pictures were present in more than 94.3% of reports. The average quality score was 5.49/7 (95% confidence interval [CI]: 5.36-5.62), 5.91/7 (95% CI: 5.80-6.03), and 5.70/7 (95% CI: 5.58-5.82) for the three centers in the three periods of interest. There was no significant difference following the introduction of the quality criteria, 2017 versus 2020, P = .054.

CONCLUSION

Fetal LVOT images were present in most of ultrasound reports but the introduction of the proposed quality criteria under human supervision seems not associated with a significant change in practice.

First-Trimester Ultrasound Imaging for Prenatal Assessment of the Extended Cardiovascular System Using the Cardiovascular System Sonographic Evaluation Algorithm (CASSEAL)

INTRODUCTION AND OBJECTIVES

To compare fetal images obtained at the first- and second-trimester ultrasound scan when applying the Cardiovascular System Sonographic Evaluation Algorithm (CASSEAL).

METHODS

Using the CASSEAL protocol, nine sequential axial views were acquired in B-mode and color Doppler at the first- and second-trimester ultrasound scans, visualizing the main components of the extended fetal cardiovascular system. Images were compared qualitatively between both trimesters.

RESULTS

We obtained comparable images for all the nine axial views described in the CASSEAL protocol, with few differences and limitations.

CONCLUSIONS

The CASSEAL protocol is reproducible in the first trimester, and could help in the early detection of fetal cardiovascular abnormalities. It represents a promising additional tool in order to increase the CHD detection rate.

Controlled Prospective Study on the Use of Systematic Simulator-Based Training with a Virtual, Moving Fetus for Learning Second-Trimester Scan: FESIM III

OBJECTIVES

To analyze the feasibility of structured ultrasound simulation training (SIM-UT) in teaching second-trimester ultrasound screening using a high-end simulator with a randomly moving fetus.

METHODS

This was a prospective, controlled trial. A trial group of 11 medical students with minimal obstetric ultrasound experience underwent 12 hours of structured SIM-UT in individual hands-on sessions within 6 weeks. Learning progress was assessed with standardized tests. Performance after 2, 4, and 6 weeks of SIM-UT was compared with two reference groups ((A) Ob/Gyn residents and consultants, and (B) highly skilled DEGUM experts). Participants were asked to acquire 23 2nd trimester planes according to ISUOG guidelines in a realistic simulation B-mode with a randomly moving fetus as quickly as possible within a 30-minute time frame. All tests were analyzed regarding the rate of appropriately obtained images and the total time to completion (TTC).

RESULTS

During the study, novices were able to improve their ultrasound skills significantly, reaching the physician level of the reference group (A) after 8 hours of training. After 12 hours of SIM-UT, the trial group performed significantly faster than the physician group (TTC: 621±189 vs. 1036±389 sec., p=0.011). Novices obtained 20 out of 23 2nd trimester standard planes without a significant time difference when compared to experts. TTC of the DEGUM reference group remained significantly faster (p<0.001) though.

CONCLUSION

SIM-UT on a simulator with a virtual, randomly moving fetus is highly effective. Novices can obtain standard plane acquisition skills close to expert level within 12 hours of self-training.

Use of artificial intelligence and deep learning in fetal ultrasound imaging

Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Shone Complex: A Case Report of Congenital Heart Disease Detected Using Point-of-care Ultrasound

INTRODUCTION

Undiagnosed congenital heart disease and management of pediatric cardiogenic shock presents a diagnostic challenge for the emergency clinician. These diagnoses are rare and require a high index of suspicion given the overlap with more common pediatric pathology. Point-of-care ultrasound can assist in differentiating these presentations. We present a case of neonatal cardiogenic shock secondary to a previously undiagnosed congenital heart disease, specifically Shone complex, detected using point-of-care ultrasound.

CASE REPORT

A six-week-old female presented with severe respiratory distress and was found to be in cardiogenic shock secondary to a previously undiagnosed congenital heart defect.

CONCLUSION

Initial diagnosis of congenital heart disease is uncommon in the emergency department, but it should be recognized by clinicians given the high associated morbidity and mortality. Point-of-care ultrasound is a powerful tool to assist in evaluating for cardiac abnormalities as an etiology for undifferentiated shock in the pediatric population.

Evaluation fetal heart in the first and second trimester: Results and limitations

Background

Cardiac heart defects affect nearly 6-12 per 1000 live births in the general population and are more frequent than common trisomies.

Aim

To assess the efficacy and technical limitations of first-trimester fetal heart evaluation in the 11-14^th-weeks' scan and comparison with the second-trimester anatomical exam by ultrasound.

Material and Method

Between April 2015 and July 2020, medical records and ultrasound data of 3295 pregnancies who underwent first-trimester fetal anatomy exams by ultrasound were reviewed retrospectively. All ultrasound exams were performed by the same two operators (TUKD, OP) with transabdominal transducers. Fetal situs, four-chamber view, outflow tracts, and three-vessel trachea view are the cornerstones of first-trimester fetal heart examination. Conventional grayscale mode and high-definition power Doppler mode were utilized. The same operators re-examined all cases between the 18 and 23 weeks of gestation by ISUOG guidelines.

Results

We performed a combined transvaginal and transabdominal approach for only 101 cases (3.06%). The mean maternal age was 31.28 ± 4.43, the median gestational age at the first-trimester ultrasound exam was 12.4 weeks, and the median CRL was 61.87 mm (range was 45.1-84 mm). Even combined approach situs, cardiac axis, and four-chamber view could not be visualized optimally in 28 cases (0.7%). Outflow tracts were visualized separately in 80% (2636 in 3295) cases. Three vessel-trachea views were obtained in 85.4% (2814 in 3295) cases by high-definition Doppler mode. There were 47 fetuses with cardiac defects in 3295 pregnancies with the known pregnancy outcome. Ten cases had abnormal karyotype results. Thirty-two fetuses with cardiac anomalies (9.7 in 1000 pregnancies) were detected in the first-trimester examination, and the remaining 15 (4.55 in 1000 pregnancies) cases were diagnosed in the second-trimester examination. The prevalence of congenital cardiac anomalies was 14.25 in 1000 pregnancies. Fifteen cases were missed in the first-trimester exam. Also, ten fetuses which had abnormal cardiac findings in the first-trimester exam were not confirmed in the second-trimester exam. Sensitivity, specificity, positive, and negative predictive values were calculated as 65.3%, 99.7%, 66.8%, and 99.67%, respectively.

Conclusion

Late first-trimester examination of the fetus is feasible and allows earlier detection of many structural abnormalities of the fetus, including congenital heart defects. Suspicious and isolated cardiac abnormal findings should be re-examined and confirmed in the second-trimester exam. Previous abdominal surgery, high BMI, and subtle cardiac defects can cause missed cardiac abnormalities.

A Diagnostic Dilemma: Transposition of the Great Arteries

Transposition of the great arteries (TGA) remains one of the most common and severe underdiagnosed congenital cardiac anomalies in the prenatal period. Unfortunately, despite advances in prenatal ultrasound screening, the detection rate of major congenital heart defects (CHDs) remains low. We present the case of a preterm male infant delivered limp with generalized cyanosis and in respiratory distress at 36 weeks gestation with postnatal echocardiography (ECHO) depicting dextro-TGA (d-TGA). Maternal prenatal targeted fetal anomaly ultrasonography at 18 weeks gestation showed abnormal right ventricle and right ventricular outflow tract. Subsequent two-time repeat fetal ECHO showed ventricular septal defect. This case represents how challenging and unrecognized critical CHDs can be. Furthermore, it highlights the need for clinicians to have a high index of suspicion when newborns present with clinical manifestations of critical CHDs and manage it accordingly to avoid severe complications.

Fetal arrhythmias case series: Experiences from a fetal screening center in Taiwan

OBJECTIVE

Fetal arrhythmias are common and in rare cases can be associated with severe mortality and morbidity. Most existing articles are focused on classification of fetal arrhythmias in referral centers. Our main objective was to analyze types, clinical characteristics, and outcomes for arrhythmia cases in general practice.

CASE REPORT

We retrospectively reviewed a case series of fetal arrhythmias in a fetal medicine clinic between September 2017 and August 2021.

FETAL ARRHYTHMIAS IN OUR SAMPLE PRESENTED BY

Ectopies (86%, n = 57), bradyarrhythmias (11%, n = 7), and tachyarrhythmias (3%, n = 2). One tachyarrhythmia case was associated with Ebstein's anomaly. Two cases of second-degree AV block received transplacental fluorinated steroid therapy with recovery of fetal cardiac rhythm in later gestation. One case of complete AV block developed hydrops fetalis.

CONCLUSION

Detection and careful stratification of fetal arrhythmias in obstetric screening is crucial. While most arrhythmias are benign and self-limited, some require prompt referral and timely intervention.

Deep learning model for prenatal congenital heart disease (CHD) screening generalizes to the community setting and outperforms clinical detection

Objective

Congenital heart defects (CHD) are still missed despite nearly universal prenatal ultrasound screening programs, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The aim of this study was to apply a previously developed DL model trained on images from a tertiary center, to fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population.

Methods

All pregnancies with isolated severe CHD in the Northwestern region of the Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region. We compared initial clinical diagnostic accuracy (made in real time), model accuracy, and performance of blinded human experts with access only to the stored images (like the model). We analyzed performance by study characteristics such as duration, quality (independently scored by study investigators), number of stored images, and availability of screening views.

Results

A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity 53 percent). Model sensitivity and specificity was 91 and 93 percent, respectively. Blinded human experts (n=3) achieved sensitivity and specificity of 55±10 percent (range 47-67 percent) and 71±13 percent (range 57-83 percent), respectively. There was a statistically significant difference in model correctness by expert-grader quality score (p=0.04). Abnormal cases included 19 lesions the model had not encountered in its training; the model's performance (15/19 correct) was not statistically significantly different on previously encountered vs. never before seen lesions (p=0.07).

Conclusions

A previously trained DL algorithm out-performed human experts in detecting CHD in a cohort in which over 50 percent of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions it had not been previously exposed to. Furthermore, when both the model and blinded human experts had access to stored images alone, the model outperformed expert humans. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD.

Trends in Delayed Diagnosis of Critical Congenital Heart Defects in an Era of Enhanced Screening, 2004-2018

OBJECTIVE

To describe trends in delayed diagnosis of critical congenital heart defects (CCHDs) with prenatal and postnatal screening advances.

STUDY DESIGN

We evaluated a retrospective cohort of live births with CCHD delivered between 2004 and 2018 from a statewide, population-based birth defects surveillance system in Massachusetts. Demographic information were obtained from vital records. We estimated timely (prenatal or birth/transfer hospital) and delayed diagnosis (after discharge) proportions by year and time periods coinciding with the transition to mandatory pulse oximetry in 2015.

RESULTS

We identified 1524 eligible CCHD cases among 1 087 027 live births. By 2018, 92% of cases received a timely diagnosis, most prenatally. From 2004 to 2018, prenatal diagnosis increased from 46% to 76% of cases, while hospital diagnosis decreased from 38% to 17%, and delayed diagnosis declined from 16% to 7%. These trends were consistent across all characteristics evaluated. Among cases without a prenatal diagnosis, the proportion with delayed diagnosis did not change over time, even after implementation of mandatory pulse oximetry screening. Prenatal detection increased the most among severe cases (treated or died in first month of life). Well-appearing newborns without prenatal diagnosis made up 79% of delayed diagnosis cases by 2015-2018. Delayed diagnosis was most common for coarctation.

CONCLUSIONS

While prenatal diagnosis of CCHD increased dramatically, there was no reduction in delayed diagnosis among postnatally diagnosed infants, even after pulse oximetry screening became mandatory. Pulse oximetry may not reduce delayed diagnosis in settings with high prenatal detection, and other strategies are needed to ensure timely diagnosis of well-appearing newborns.

Are there foetal extracellular vesicles in maternal blood? Prospects for diagnostic biomarker discovery

Prenatal diagnosis of congenital disease improves clinical outcomes; however, as many as 50% of congenital heart disease cases are missed by current ultrasound screening methods. This indicates a need for improved screening technology. Extracellular vesicles (EVs) have attracted enormous interest in recent years for their potential in diagnostics. EVs mediate endocrine signalling in health and disease and are known to regulate aspects of embryonic development. Here, we critically evaluate recent evidence suggesting that EVs released from the foetus are able to cross the placenta and enter the maternal circulation. Furthermore, EVs from the mother appear to be transported in the reverse direction, whilst the placenta itself acts as a source of EVs. Experimental work utilising rodent models employing either transgenically encoded reporters or application of fluorescent tracking dyes provide convincing evidence of foetal-maternal crosstalk. This is supported by clinical data demonstrating expression of placental-origin EVs in maternal blood, as well as limited evidence for the presence of foetal-origin EVs. Together, this work raises the possibility that foetal EVs present in maternal blood could be used for the diagnosis of congenital disease. We discuss the challenges faced by researchers in translating these basic science findings into a clinical non-invasive prenatal test.

Improvement of diagnostic efficiency in fetal congenital heart disease using fetal intelligent navigation echocardiography by less-experienced operators

OBJECTIVE

This study investigated the feasibility and accuracy of fetal intelligent navigation echocardiography (FINE) for the prenatal diagnosis of congenital heart disease (CHD) by inexperienced and experienced operators.

METHOD

In this prospective study, all volume data sets from 120 fetuses with a broad spectrum of CHD were acquired using spatiotemporal image correlation technology. The prenatal diagnostic procedures were performed by two operators with different experience (beginner: 1 year and expert: 15 years) using FINE and traditional fetal echocardiography. Data were analyzed on the time of examination and acquisition of results.

RESULTS

Diagnoses made by FINE and traditional echocardiography were completely consistent with the final diagnosis of CHD in 98 (81.66%) versus 20 (16.66%) (P < 0.001) beginners and 87.50% (n = 105) versus 101 (84.16%) experts, respectively. On the contrary, there was significant difference using traditional echocardiography (16.66% versus 84.16%, P < 0.001) by two examiners. Furthermore, the examination time decreased when using FINE compared with using traditional echocardiography (beginner operators: 4.54 ± 1.03 min versus 20.58 ± 3.36 min, P < 0.001; expert operators: 3.89 ± 0.96 min versus 12.73 ± 1.62 min, P < 0.001).

CONCLUSION

Based on our results, a prenatal diagnosis of CHD can be made with high feasibility and accuracy using FINE compared with traditional fetal echocardiography for beginner operators.

Prenatal detection of aortic coarctation in a well-organized screening setting: Are we there yet?

OBJECTIVE

We aimed to assess current prenatal detection rate (DR) of aortic coarctation (CoA) and its impact on neonatal outcome in the Netherlands to evaluate the efficacy of the Dutch screening protocol in which the cardiac four-chamber view, outflow tracts and three-vessel view are compulsory.

METHODS

All prenatally and postnatally diagnosed CoA cases between 2012 and 2021 were extracted from our PRECOR-registry. Annual DRs were calculated with a focus on the trend over time and attributing factors for detection. Postnatal outcome was compared between prenatally detected and undetected cases.

RESULTS

49/116 cases (42.2%) were detected prenatally. A higher chance of detection was found for cases with extracardiac malformations (71.4%; p = 0.001) and the more severe cases with an aortic arch hypoplasia and/or ventricular septal defect (63.2%; p = 0.001). Time-trend analysis showed no improvement in DR over time (p = 0.33). Undetected cases presented with acute circulatory shock in 20.9% and were more likely to have severe lactic acidosis (p = 0.02) and impaired cardiac function (p < 0.001) before surgery.

CONCLUSION

Even in a well-organized screening program, the DR of CoA still requires improvement, especially in isolated cases. The increased risk of severe lactic acidosis in undetected cases stresses the need for urgent additions to the current screening program, such as implementation of the three-vessel trachea view and measurement of outflow tracts.