Prospective evaluation of the fetal heart using Fetal Intelligent Navigation Echocardiography (FINE)

Artificial intelligence in fetal echocardiography: Recent advances and future prospects

In the past few decades, great progress has been made in prenatal diagnosis of congenital heart disease (CHD). Fetal echocardiography is recognized as the main prenatal screening and diagnostic tool that can accurately detect approximately 85 % of fetal cardiac abnormalities. Evaluation of the fetal heart remains a major challenge in prenatal ultrasound screening and diagnosis due to fetal position, involuntary movement, small and complex fetal cardiac anatomy, maternal abdominal wall conditions, and lack of expertise in fetal echocardiography by some physicians engaged in obstetric ultrasound. Artificial intelligence (AI) can automate and standardize the display of each diagnostic section of the fetal heart and thus contribute to accurate diagnosis, which significantly optimizes the clinical application of fetal echocardiography. In this review, we not only clarify the role of AI but also highlight its significance and future solutions in the field of fetal echocardiography.

Advances in the Application of Artificial Intelligence in Fetal Echocardiography

Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.

E-Health: A Game Changer in Fetal and Neonatal Cardiology?

Technological advancements have greatly impacted the healthcare industry, including the integration of e-health in pediatric cardiology. The use of telemedicine, mobile health applications, and electronic health records have demonstrated a significant potential to improve patient outcomes, reduce healthcare costs, and enhance the quality of care. Telemedicine provides a useful tool for remote clinics, follow-up visits, and monitoring for infants with congenital heart disease, while mobile health applications enhance patient and parents' education, medication compliance, and in some instances, remote monitoring of vital signs. Despite the benefits of e-health, there are potential limitations and challenges, such as issues related to availability, cost-effectiveness, data privacy and security, and the potential ethical, legal, and social implications of e-health interventions. In this review, we aim to highlight the current application and perspectives of e-health in the field of fetal and neonatal cardiology, including expert parents' opinions.

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

Application of Semiautomatic Fetal Intelligent Navigation Echocardiography (FINE) in Twin Pregnancies: Half the Work or Twice the Effort?

OBJECTIVE

To assess the performance of fetal intelligent navigation echocardiography (FINE, 5D Heart™) for automated volumetric investigation of the fetal heart in twin pregnancies.

METHODS

Three hundred twenty-eight twin fetuses underwent fetal echocardiography in the second and third trimesters. Spatiotemporal image correlation (STIC) volumes were obtained for a volumetric investigation. The volumes were analyzed using the FINE software, and the data were investigated regarding image quality and many properly reconstructed planes.

RESULTS

Three hundred and eight volumes underwent final analysis. 55.8% of the included pregnancies were dichorionic twin pregnancies, and 44.2% were monochorionic twin pregnancies. The mean gestational age (GA) was 22.1 weeks, and the mean maternal BMI was 27.3 kg/m². The STIC-volume acquisition was successful in 100.0% and 95.5% of cases. The overall depiction rates of FINE were 96.5% (twin 1) and 94.7% (twin 2), respectively (p = 0.0849, not significant). In 95.9% (twin 1) and 93.9% (twin 2), at least 7 planes were reconstructed properly (p = 0.6056, not significant).

CONCLUSION

Our results indicate that the FINE technique used in twin pregnancies is reliable. No significant difference between the depiction rates of twin 1 and twin 2 could be detected. In addition, the depiction rates are as high as those derived from singleton pregnancies. Due to the challenges of fetal echocardiography in twin pregnancies (i.e., greater rates of cardiac anomaly and more difficult scans), the FINE technique might be a valuable tool to improve the quality of medical care in those pregnancies.

Prenatal Diagnosis and Fetopsy Validation of Complete Atrioventricular Septal Defects Using the Fetal Intelligent Navigation Echocardiography Method

(1) Background: Artificial Intelligence (AI) is a modern tool with numerous applications in the medical field. The case series reported here aimed to investigate the diagnostic performance of the fetal intelligent navigation echocardiography (FINE) method applied for the first time in the prenatal identification of atrioventricular septal defects (AVSD). This congenital heart disease (CHD) is associated with extracardiac anomalies and chromosomal abnormalities. Therefore, an early diagnosis is essential to advise parents and make adequate treatment decisions. (2) Methods: Four fetuses diagnosed with AVSD via two-dimensional (2D) ultrasound examination in the second trimester were enrolled. In all cases, the parents chose to terminate the pregnancy. Since the diagnosis of AVSD with 2D ultrasound may be missed, one or more four-dimensional (4D) spatiotemporal image correlation (STIC) volume datasets were obtained from a four-chamber view. The manual navigation enabled by the software is time-consuming and highly operator-dependent. (3) Results: FINE was applied to these volumes and nine standard fetal echocardiographic views were generated and optimized automatically, using the assistance of the virtual intelligent sonographer (VIS). Here, 100% of the four-chamber views, and after the VISA System application the five-chamber views, of the diagnostic plane showed the atrioventricular septal defect and a common AV valve. The autopsies of the fetuses confirmed the ultrasound results. (4) Conclusions: By applying intelligent navigation technology to the STIC volume datasets, 100% of the AVSD diagnoses were detected.

Fetal intelligent navigation echocardiography (FINE) has superior performance compared to manual navigation of the fetal heart by non-expert sonologists

OBJECTIVES

Manual and intelligent navigation (i.e. fetal intelligent navigation echocardiography or FINE) by the operator are two methods to obtain standard fetal cardiac views from spatiotemporal image correlation (STIC) volumes. The objective was to compare the performance between manual and intelligent navigation (FINE) of the fetal heart by non-expert sonologists.

METHODS

In this prospective observational study, ten sonologists underwent formal training on both navigational methods. Subsequently, they were tested on their ability to obtain nine cardiac views from five STIC volumes of normal fetal hearts (19-28 gestational weeks) using such methods. The following parameters were determined for both methods: (1) success rate of obtaining nine cardiac views; (2) mean time to obtain nine cardiac views per sonologist; and (3) maximum number of cardiac views successfully obtained for each STIC volume.

RESULTS

All fetal cardiac images obtained from 100 STIC volumes (50 for each navigational method) were reviewed by an expert in fetal echocardiography. Compared to manual navigation, FINE was associated with a significantly: (1) higher success rate of obtaining eight (excluding the abdomen view) appropriate cardiac views (92-100% vs. 56-88%; all p<0.05); (2) shorter mean time (minute:seconds) to obtain nine cardiac views (2:11 ± 0:37 vs. 15:49 ± 7:44; p<0.0001); and (3) higher success rate of obtaining all nine cardiac views for a given STIC volume (86 vs. 14%; p<0.001).

CONCLUSIONS

When performed by non-expert sonologists, intelligent navigation (FINE) had a superior performance compared to manual navigation of the normal fetal heart. Specifically, FINE obtained appropriate fetal cardiac views in 92-100% of cases.

Maximal Reduction of STIC Acquisition Time for Volumetric Assessment of the Fetal Heart—Benefits and Limitations of Semiautomatic Fetal Intelligent Navigation Echocardiography (FINE) Static Mode

(1) Objective: To scrutinize the reliability and the clinical value of routinely used fetal intelligent navigation echocardiography (FINE) static mode (5DHeartStatic™) for accelerated semiautomatic volumetric assessment of the normal fetal heart. (2) Methods: In this study, a total of 296 second and third trimester fetuses were examined by targeted ultrasound. Spatiotemporal image correlation (STIC) volumes of the fetal heart were acquired for further volumetric assessment. In addition, all fetal hearts were scanned by a fast acquisition time volume (1 s). The volumes were analyzed using the FINE software. The data were investigated regarding the number of properly reconstructed planes and cardiac axis. (3) Results: A total of 257 volumes were included for final analysis. The mean gestational age (GA) was 23.9 weeks (14.3 to 37.7 weeks). In 96.9 (standard acquisition time, FINE standard mode) and 94.2% (fast acquisition time, FINE static mode) at least seven planes were reconstructed properly (p = 0.0961, not significant). Regarding the overall depiction rate, the standard mode was able to reconstruct 96.9% of the planes properly, whereas the static mode showed 95.2% of the planes (p = 0.0098). Moreover, there was no significant difference between the automatic measurement of the cardiac axis (37.95 + 9.14 vs. 38.00 + 8.92 degrees, p = 0.8827, not significant). (4) Conclusions: Based on our results, the FINE static mode technique is a reliable method. It provides similar information of the cardiac anatomy compared to conventional STIC volumes assessed by the FINE method. The FINE static mode has the potential to minimize the influence of motion artifacts during volume acquisition and might therefore be helpful concerning volumetric cardiac assessment in daily routine.

Artificial intelligence in perinatal diagnosis and management of congenital heart disease

Prenatal diagnosis and management of congenital heart disease (CHD) has progressed substantially in the past few decades. Fetal echocardiography can accurately detect and diagnose approximately 85% of cardiac anomalies. The prenatal diagnosis of CHD results in improved care, with improved risk stratification, perioperative status and survival. However, there is much work to be done. A minority of CHD is actually identified prenatally. This seemingly incongruous gap is due, in part, to diminished recognition of an anomaly even when present in the images and the need for increased training to obtain specialized cardiac views. Artificial intelligence (AI) is a field within computer science that focuses on the development of algorithms that "learn, reason, and self-correct" in a human-like fashion. When applied to fetal echocardiography, AI has the potential to improve image acquisition, image optimization, automated measurements, identification of outliers, classification of diagnoses, and prediction of outcomes. Adoption of AI in the field has been thus far limited by a paucity of data, limited resources to implement new technologies, and legal and ethical concerns. Despite these barriers, recognition of the potential benefits will push us to a future in which AI will become a routine part of clinical practice.

New and advanced features of fetal intelligent navigation echocardiography (FINE) or 5D heart

Congenital heart disease (CHD) is the leading organ-specific birth defect, as well as the leading cause of infant morbidity and mortality from congenital malformations. Therefore, a comprehensive screening examination of the fetal heart should be performed in all women to maximize the detection of CHD. Four-dimensional sonography with spatiotemporal image correlation (STIC) technology displays a cine loop of a complete single cardiac cycle in motion. A novel method known as Fetal Intelligent Navigation Echocardiography (or FINE) was previously developed to interrogate STIC volume datasets using "intelligent navigation" technology. Such method allows the automatic display of nine standard fetal echocardiography views required to diagnose most cardiac defects. FINE considerably simplifies fetal cardiac examinations and reduces operator dependency. It has both high sensitivity and specificity for the detection of CHD. Indeed, FINE has been integrated into several commercially available ultrasound platforms.Recently, eight novel and advanced features have been developed for the FINE method and they will be described herein. Such features can be categorized based upon their broad goals. The first goal is to simplify FINE further, and consists of the following features: (1) Auto fetal positioning (or FINE align); (2) Skip points; (3) Predictive cursor; (4) Static mode volume; and (5) Breech sweep. The second goal is to allow quantitative measurements to be performed on the cardiac views generated by FINE: (6) Automatic cardiac axis; and (7) Cardiac biometry. Finally, the last goal is to improve the success of obtaining fetal echocardiography view(s); and consists of (8) Maestro planar navigation.

Feasibility of Semiautomatic Fetal Intelligent Navigation Echocardiography for Different Fetal Spine Positions: A Matter of "Time"?

OBJECTIVES

We investigated the feasibility of a semiautomatic approach for assessments of the fetal heart (fetal intelligent navigation echocardiography [FINE]) in cases of optimal and unfavorable fetal spine positions.

METHODS

In this study, a total of 1693 spatiotemporal image correlation volumes of first-, second-, and third-trimester fetuses were evaluated by experts using the FINE approach. The data were analyzed regarding proper reconstruction of the diagnostic cardiac planes depending on the fetal spine position.

RESULTS

A total of 1531 volumes were included. The volumes were divided into 4 groups depending on the fetal spine position: 5-7 o'clock, 4 + 8 o'clock, 3 + 9 o'clock, and 2 + 10 o'clock. In total, 93.2% of the diagnostic planes were displayed properly. Between 5 and 7 o'clock, 94.9% of the diagnostic planes were displayed properly. The correct depiction rates in the other groups were 92.4% (4 + 8 o'clock; n = 538; P = 0.0027), 88.3% (3 + 9 o'clock; n = 156; P < .0001), and 87.3% (2 + 10 o'clock; n = 41; P = .0139). In total, the highest dropout rates were found in the sagittal planes: ductal arch, 13.9%; aortic arch, 10.5%; and venae cavae, 12.0%.

CONCLUSIONS

Based on our results, the FINE technique is an effective method, but its feasibility depends on the fetal position. The use of this semiautomatic work flow-based approach supports evaluation of the fetal heart in a standardized manner. Semiautomatic evaluation of the fetal heart might be useful in facilitating the detection of fetal cardiac anomalies.

Diagnostic performance of fetal intelligent navigation echocardiography (FINE) in fetuses with double-outlet right ventricle (DORV)

The main objective of this study was to investigate the diagnostic performance of FINE in generating and displaying 3 specific abnormal fetal echocardiography views such as left ventricular outflow tract (LVOT) view, right ventricular outflow tract (RVOT) view, and 3-vessels and trachea (3VT) view in fetuses with double-outlet right ventricle (DORV). In this prospective study, thirty fetuses diagnosed with DORV by fetal echocardiography in the second and third trimesters were enrolled. One or more STIC volume data-sets were collected from the 4-chamber view as initial view for each fetus, one optimal volume per fetus was selected for on-line analysis using FINE, and the diagnosis plane image was optimized using the Virtual Intelligent Sonographer Assistance (VIS-assistance).The visualization rates of 3 specific abnormal fetal echocardiography views of DORV and key diagnostic elements were calculated. One or more STIC volumes (n = 30 total) were obtained in 25 patients. A single STIC volume per patient was analyzed using the FINE method. FINE was able to successfully generate and display 3 specific abnormal fetal echocardiography views. The display rates of the 3 specific abnormal fetal echocardiography views (3VT, LVOT, RVOT) were 84.0%, 76.0% and 84.0%, respectively. By applying intelligent navigation technology to STIC volume data-sets, the FINE method can successfully generate three specific abnormal cardiac fetal echocardiography diagnostic views in fetuses with DORV, the FINE method can be used for screening and remote consultation of fetal DORV.

Novel foetal echocardiographic image processing software (5D Heart) improves the display of key diagnostic elements in foetal echocardiography

Background

To evaluate the clinical value of foetal intelligent navigation echocardiography (5D Heart) for the display of key diagnostic elements in basic sections.

Methods

3D volume datasets of 182 normal singleton foetuses were acquired with a four chamber view by using a volume probe. After processing the datasets by using 5D Heart, eight cardiac diagnostic planes were demonstrated, and the image qualities of the key diagnostic elements were graded by 3 doctors with different experiences in performing foetal echocardiography.

Results

A total of 231 volume datasets acquired from the 182 normal foetuses were used for 5D Heart analysis and display. The success rate of 8 standard diagnostic views was 88.2%, and the success rate of each diagnostic view was 55.8–99.2% and 70.7–99.0% for the random four chamber view as the initial section and for the apical four chamber view as the initial section, respectively. The success rate of each diagnostic element in the 8 diagnostic sections obtained by 5D Heart was 58.9%~ 100%. Excellent agreement was found between experienced sonographers and less-experienced sonographers (kappa> 0.769). Inter- and intra-observer agreement were substantial to near-perfect, kappa values ranging from 0.612 to 1.000 (Cohen’s kappa).

Conclusions

5D Heart can significantly improve the image quality of key diagnostic elements in foetal echocardiography with low operator dependency and good reproducibility.

Is Fetal Intelligent Navigation Echocardiography Helpful in Screening for d-Transposition of the Great Arteries?

OBJECTIVES

To evaluate the performance of fetal intelligent navigation echocardiography (FINE) applied to spatiotemporal image correlation (STIC) volumes in generating 3 specific abnormal cardiac views (left ventricular outflow tract, right ventricular outflow tract, and 3-vessel and trachea) used to screen for d-transposition of the great arteries (d-TGA).

METHODS

In this prospective study, 1 or more STIC volumes were obtained from the 4-chamber view in 34 second- and third-trimester fetuses with d-TGA. Each appropriate STIC volume was evaluated by STICLoop (Samsung Medison, Seoul, Korea) before applying the FINE method. One optimal volume per fetus was selected by observers. The visualization rates of the 3 specific abnormal cardiac views of d-TGA and their diagnostic elements were calculated, and the reliability between 2 observers was verified by the intraclass correlation coefficient.

RESULTS

Fetal intelligent navigation echocardiography applied to STIC volume data sets of fetuses with d-TGA successfully generated the 3 specific abnormal cardiac views in the following manner for 2 observers: 75.0% (n = 21) for the left ventricular outflow tract, 89.2% (n = 25) for the right ventricular outflow tract, and 85.7% (n = 24) for the 3-vessel and trachea view. Twenty-four (85.7%) of the STIC volume data sets showed 2 or 3 of the abnormal cardiac views. The interobserver intraclass correlation coefficients between the 2 observers ranged from 0.842 to 1.000 (95% confidence interval), indicating almost perfect reliability for the 2 observers.

CONCLUSIONS

In cases of d-TGA, the FINE method has a high success rate in generating 3 specific abnormal cardiac views and therefore can be performed to screen for this congenital defect.

Semiautomatic Fetal Intelligent Navigation Echocardiography Has the Potential to Aid Cardiac Evaluations Even in Less Experienced Hands

OBJECTIVES

To investigate the interobserver and intraobserver variability and corresponding learning curve in a semiautomatic approach for a standardized assessment of the fetal heart (fetal intelligent navigation echocardiography [FINE]).

METHODS

A total of 30 stored spatiotemporal image correlation volume data sets of second-trimester fetuses were evaluated by 3 physicians with different levels of expertise in fetal echocardiography by using the FINE approach. Data were analyzed regarding the examination time and proper reconstruction of the diagnostic cardiac planes. The completions and numbers of correct depictions of all diagnostic planes were evaluated by a blinded expert (time t0). To determine interobserver and intraobserver variability, the volumes were reassessed after a 4-week training interval (time t1).

RESULTS

All operators were able to perform the investigation on all 30 volumes. At t0, the interobserver variability between the beginner and both the advanced (P = .0013) and expert (P < .0001) examiners was high. Focusing on intraobserver variability at t1, the beginner showed a marked improvement (P = .0087), whereas in advanced and expert hands, no further improvement regarding proper achievement of all diagnostic planes could be noticed (P > .999; P = .8383). The beginner also showed improvement in the mean investigation time (t0, 82.8 seconds; t1, 73.4 seconds; P = .0895); nevertheless, the advanced and expert examiners were faster in completing the examination (t1, advanced, 20.9 seconds; expert, 28.3 seconds; each P < .0001).

CONCLUSIONS

Based on our results, the FINE technique is a reliable and easily learned method. The use of this semiautomatic work flow-based approach supports evaluation of the fetal heart in a standardized and time-saving manner. A semiautomatic evaluation of the fetal heart might be useful in facilitating the detection of fetal cardiac anomalies.

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study

Background

Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound.

Objective

This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality.

Methods

Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system.

Results

Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively).

Conclusions

The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

How to obtain diagnostic planes of the fetal central nervous system using three-dimensional ultrasound and a context-preserving rendering technology

The antenatal evaluation of the fetal central nervous system (CNS) is among the most difficult tasks of prenatal ultrasound (US), requiring technical skills in relation to ultrasound and image acquisition as well as knowledge of CNS anatomy and how this changes with gestation. According to the International Guidelines for fetal neurosonology, the basic assessment of fetal CNS is most frequently performed on the axial planes, whereas the coronal and sagittal planes are required for the multiplanar evaluation of the CNS within the context of fetal neurosonology. It can be even more technically challenging to obtain "nonaxial" views with 2-dimensional (2D) US. The modality of 3-dimensional (3D) US has been suggested as a panacea to overcome the technical difficulties of achieving nonaxial views. The lack of familiarity of most sonologists with the use of 3D US and its related processing techniques may preclude its use even where it could play an important role in complementing antenatal 2D US assessment. Furthermore, once a 3D volume has been acquired, proprietary software allows it to be processed in different ways, leading to multiple ways of displaying and analyzing the same anatomical imaging or plane. These are difficult to learn and time consuming in the absence of specific training. In this article, we describe the key steps for volume acquisition of a 3D US volume, manipulation, and processing with reference to images of the fetal CNS, using a newly developed context-preserving rendering technique.

Fetal Intelligent Navigation Echocardiography (FINE) Detects 98% of Congenital Heart Disease

OBJECTIVE

Fetal intelligent navigation echocardiography (FINE) is a novel method that automatically generates and displays 9 standard fetal echocardiographic views in normal hearts by applying intelligent navigation technology to spatiotemporal image correlation (STIC) volume data sets. The main objective was to determine the sensitivity and specificity of FINE in the prenatal detection of congenital heart disease (CHD).

METHODS

A case-control study was conducted in 50 fetuses with a broad spectrum of CHD (cases) and 100 fetuses with normal hearts (controls) in the second and third trimesters. Using 4-dimensional ultrasound with STIC technology, volume data sets were acquired. After all identifying information was removed, the data sets were randomly distributed to a different investigator for analysis using FINE. The sensitivity and specificity for the prenatal detection of CHD, as well as positive and negative likelihood ratios were determined.

RESULTS

The diagnostic performance of FINE for the prenatal detection of CHD was: sensitivity of 98% (49 of 50), specificity of 93% (93 of 100), positive likelihood ratio of 14, and negative likelihood ratio of 0.02. Among cases with confirmed CHD, the diagnosis with use of FINE completely matched the final diagnosis in 74% (37 of 50); minor discrepancies were seen in 12% (6 of 50), and major discrepancies were seen in 14% (7 of 50).

CONCLUSIONS

This is the first time the sensitivity and specificity of the FINE method in fetuses with normal hearts and CHD in the second and third trimesters has been reported. Because FINE identifies a broad spectrum of CHD with 98% sensitivity, this method could be used prenatally to screen for and diagnose CHD.

4D fetal echocardiography-An update

With the introduction of the electronic 4-dimensional and spatial-temporal image Correlation (e-STIC), it is now possible to obtain large volume datasets of the fetal heart that are virtually free of artifact. This allows the examiner to use a number of imaging modalities when recording the volumes that include two-dimensional real time, power and color Doppler, and B-flow images. Once the volumes are obtained, manipulation of the volume dataset allows the examiner to recreate views of the fetal heart that enable examination of cardiac anatomy. The value of this technology is that a volume of the fetal heart can be obtained, irrespective of the position of the fetus in utero, and manipulated to render images for interpretation and diagnosis. This article presents a summary of the various imaging techniques and provides clinical examples of its application used for prenatal diagnosis of congenital heart defects and abnormal cardiac function.

Prenatal diagnosis of tetralogy of Fallot with pulmonary atresia using: Fetal Intelligent Navigation Echocardiography (FINE)

Tetralogy of Fallot with pulmonary atresia, a severe form of tetralogy of Fallot, is characterized by the absence of flow from the right ventricle to the pulmonary arteries. This cardiac abnormality is challenging and complex due to its many different anatomic variants. The main source of variability is the pulmonary artery anatomy, ranging from well-formed, confluent pulmonary artery branches to completely absent native pulmonary arteries replaced by major aorto-pulmonary collateral arteries (MAPCAs) that provide all of the pulmonary blood flow. Since the four-chamber view is usually normal on prenatal sonography, the diagnosis may be missed unless additional cardiac views are studied. Fetal Intelligent Navigation Echocardiography (FINE) is a novel method developed recently that allows automatic generation of nine standard fetal echocardiography views in normal hearts by applying "intelligent navigation" technology to spatiotemporal image correlation volume datasets. We report herein for the first time, two different cases of tetralogy of Fallot with pulmonary atresia having variable sources of pulmonary blood flow in which the prenatal diagnosis was made successfully using the FINE method. Virtual Intelligent Sonographer Assistance (VIS-Assistance^®) and automatic labeling (both features of FINE) were very helpful in making such diagnosis.

Advances in fetal echocardiography

The development of fetal echocardiography and success in prenatal cardiac screening programs over the past 30 years has been driven by technical innovation and influenced by the different approaches of the various specialties practicing it. Screening for congenital heart defects no longer focuses on examining a limited number of pregnant women thought to be at increased risk, but instead forms an integrated part of a high-quality anatomical ultrasound performed in the second trimester using the 'five-transverse view' protocol. A prenatal diagnosis is feasible in almost all cardiac lesions and the advantages to parents and to health professionals are well recognized. Prenatal evaluation can usually determine the level of care required at delivery, thereby reducing perinatal morbidity. However, only half of the babies undergoing surgery within the first year of life have a prenatal detection, and practical training programs to support and provide feedback to sonographers remain essential for continued improvement.

Electronic spatiotemporal image correlation improves four-dimensional fetal echocardiography

OBJECTIVES

To compare the efficiency of electronic spatiotemporal image correlation (eSTIC) with that of conventional STIC to acquire four-dimensional (4D) fetal cardiac volumes of diagnostic quality.

METHODS

This was a randomized controlled trial of 100 patients in mid-gestation with normal sonograms. In half of the cases, STIC volumes of the fetal heart were obtained with a conventional mechanical 4D probe and in the remaining cases eSTIC volumes were obtained with an electronic 4D probe. Examinations were kept within the timeframe allotted for a standard examination of fetal anatomy, and a maximum of two attempts were made at obtaining a 4D cardiac volume. Datasets were stored on a computer and subsequently analyzed and categorized as being of optimal, satisfactory or inadequate quality, depending on whether or not it was possible to perform an extended basic cardiac examination, including obtaining a three vessels and trachea view, as well as a clear reconstruction of both the aortic and ductal arches in the sagittal plane.

RESULTS

The eSTIC volume datasets were more frequently of optimal or satisfactory diagnostic quality compared with conventional STIC (94% vs 76%, P < 0.0001). Failure to obtain an eSTIC volume of adequate quality was in all cases the consequence of an unfavorable position of the fetus.

CONCLUSIONS

Compared with a standard mechanical probe, the electronic 4D probe facilitates acquisition of sonographic cardiac volumes in mid-trimester fetuses. In our hands, eSTIC volumes of optimal or satisfactory diagnostic quality, allowing a detailed offline evaluation of the fetal heart, were obtained in more than 90% of cases within the time frame of a standard examination of fetal anatomy. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Color and power Doppler combined with Fetal Intelligent Navigation Echocardiography (FINE) to evaluate the fetal heart

OBJECTIVE

To evaluate the performance of color and bidirectional power Doppler ultrasound combined with Fetal Intelligent Navigation Echocardiography (FINE) in examining the fetal heart.

METHODS

A prospective cohort study was conducted of fetuses in the second and third trimesters with a normal heart or with congenital heart disease (CHD). One or more spatiotemporal image correlation (STIC) volume datasets, combined with color or bidirectional power Doppler (S-flow) imaging, were acquired in the apical four-chamber view. Each successfully obtained STIC volume was evaluated by STICLoop™ to determine its appropriateness before applying the FINE method. Visualization rates for standard fetal echocardiography views using diagnostic planes and/or Virtual Intelligent Sonographer Assistance (VIS-Assistance®) were calculated for grayscale (removal of Doppler signal), color Doppler and S-flow Doppler. In four cases with CHD (one case each of tetralogy of Fallot, hypoplastic left heart and coarctation of the aorta, interrupted inferior vena cava with azygos vein continuation and asplenia, and coarctation of the aorta with tricuspid regurgitation and hydrops), the diagnostic potential of this new technology was presented.

RESULTS

A total of 169 STIC volume datasets of the normal fetal heart (color Doppler, n = 78; S-flow Doppler, n = 91) were obtained from 37 patients. Only a single STIC volume of color Doppler and/or a single volume of S-flow Doppler per patient were analyzed using FINE. Therefore, 60 STIC volumes (color Doppler, n = 27; S-flow Doppler, n = 33) comprised the final study group. Median gestational age at sonographic examination was 23 (interquartile range, 21-27.5) weeks. Color Doppler FINE generated nine fetal echocardiography views (grayscale) using (1) diagnostic planes in 73-100% of cases, (2) VIS-Assistance in 100% of cases, and (3) a combination of diagnostic planes and/or VIS-Assistance in 100% of cases. The rate of generating successfully eight fetal echocardiography views with appropriate color and S-flow Doppler information was 89-100% and 91-100% of cases, respectively, using a combination of diagnostic planes and/or VIS-Assistance. However, the success rate for the ninth echocardiography view (i.e. superior and inferior venae cavae) was 33% and 30% for color and S-flow Doppler, respectively. In all four cases of CHD, color Doppler FINE demonstrated evidence of abnormal fetal cardiac anatomy and/or hemodynamic flow.

CONCLUSIONS

The FINE method applied to STIC volumes of normal fetal hearts acquired with color or bidirectional power Doppler information can generate successfully eight to nine standard fetal echocardiography views (via grayscale, color Doppler or power Doppler) in the second and third trimesters. In cases of CHD, color Doppler FINE demonstrates successfully abnormal anatomy and/or Doppler flow characteristics. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Prenatal Diagnosis of Dextrocardia with Complex Congenital Heart Disease Using Fetal Intelligent Navigation Echocardiography (FINE) and a Literature Review

Fetal dextrocardia is a type of cardiac malposition where the major axis from base to apex points to the right side. This condition is usually associated with a wide spectrum of complex cardiac defects. As a result, dextrocardia is conceptually difficult to understand and diagnose on prenatal ultrasound. The advantage of four-dimensional sonography with spatiotemporal image correlation (STIC) is that this modality can facilitate fetal cardiac examination. A novel method known as fetal intelligent navigation echocardiography (FINE) allows automatic generation of nine standard fetal echocardiography views in normal hearts by applying intelligent navigation technology to STIC volume datasets. In fetuses with congenital heart disease, FINE is also able to demonstrate abnormal cardiac anatomy and relationships when there is normal cardiac axis and position. However, this technology has never been applied to cases of cardiac malposition. We report herein for the first time, a case of fetal dextrocardia and situs solitus with complex congenital heart disease in which the FINE method was invaluable in diagnosing multiple abnormalities and defining complex anatomic relationships. We also review the literature on prenatal sonographic diagnosis of dextrocardia (with an emphasis on situs solitus), as well as tricuspid atresia with its associated cardiac features.

Is the short axis view of the fetal heart useful in improving the diagnostic accuracy of outlet ventricular septal defects?

OBJECTIVES

Outlet ventricular septal defects (VSDs) are usually suspected on the five-chamber view of the fetal heart; however, postnatal confirmation occurs only in a small number of cases. The aim of this study was to evaluate if the systematic assessment of the short axis view may improve the prediction of prenatally detected outlet VSDs.

METHODS

Cases where isolated outlet VSD was suspected on the five-chamber view were assessed by using the short axis scanning plane for confirmation of the defect. Postnatal assessment was performed within 2 weeks from birth at our Paediatric Cardiology Unit.

RESULTS

An outlet VSD was suspected at five-chamber view in 23 fetuses. Postnatal confirmation of the VSD occurred in 14 cases where the defect was prenatally detected both on the five chamber and the short axis views. VSDs were not confirmed at postnatal assessment only in two cases where outlet VSD was suspected on both views, whereas all the seven cases with a suspected VSD on the five-chamber view only turned out to be false positives.

CONCLUSION

The short axis view of the fetal heart seems useful in confirming the presence of outlet VSDs. © 2016 John Wiley & Sons, Ltd.

Evaluation of fetal cardiac valve anomalies by four-dimensional echocardiography with spatiotemporal image correlation (4DSTIC)

BACKGROUND

Prenatal diagnosis of cardiac valve anomalies challenged most screening sonographers. The purpose of the study was to evaluate the use of four-dimensional echocardiography with spatiotemporal image correlation (4DSTIC) in detecting normal and abnormal fetal cardiac valves.

METHODS

Forty-three cases of confirmed cardiac valve anomalies identified by two-dimensional echocardiography (2DE) were retrospectively reviewed in this study. Additional 121 confirmed normal fetuses were included as controls. Four-dimensional volumes were acquired from each fetus using a transverse sweep. Four-dimensional rendered images were retrieved from the volumes for each of the cardiac valves for the normal fetuses and for the intended valves for fetuses with valve malformations.

RESULTS

The visualization rates of cardiac valves retrieved from 4D volumes in the normal fetuses ranged from 72.5% to 97.5% before 33 gestational weeks and from 46.3% to 80.5% in late pregnancy. Furthermore, 4D rendered images were successfully obtained in 38 of 43 (88.4%) fetuses with cardiac valve lesions.

CONCLUSIONS

The 4D images and cine loops displayed the valves anatomy vividly in both normal and abnormal fetuses, including some subtle malformations which were not identified by traditional 2DE. The standardized protocol we propose herein was important in obtaining the 4D images from the volumes. The 4D modality allows a better visualization of fetal cardiac valves and should be considered a valuable addition to traditional 2DE imaging.

A Prospective Study of the Use of Fetal Intelligent Navigation Echocardiography (FINE) to Obtain Standard Fetal Echocardiography Views

OBJECTIVE

To evaluate the performance of Fetal Intelligent Navigation Echocardiography (FINE) applied to spatiotemporal image correlation (STIC) volume datasets of the normal fetal heart in generating standard fetal echocardiography views.

METHODS

In this prospective cohort study of patients with normal fetal hearts (19-30 gestational weeks), one or more STIC volume datasets were obtained of the apical four-chamber view. Each STIC volume successfully obtained was evaluated by STICLoop™ to determine its appropriateness before applying the FINE method. Visualization rates for standard fetal echocardiography views using diagnostic planes and/or Virtual Intelligent Sonographer Assistance (VIS-Assistance®) were calculated.

RESULTS

One or more STIC volumes (total n = 463) were obtained from 246 patients. A single STIC volume per patient was analyzed using the FINE method. In normal cases, FINE was able to generate nine fetal echocardiography views using: (1) diagnostic planes in 76-100% of the cases, (2) VIS-Assistance® in 96-100% of the cases, and (3) a combination of diagnostic planes and/or VIS-Assistance® in 96-100% of the cases.

CONCLUSION

FINE applied to STIC volumes can successfully generate nine standard fetal echocardiography views in 96-100% of cases in the 2nd and 3rd trimesters. This suggests that the technology can be used as a method of screening for congenital heart disease.

How to Acquire Cardiac Volumes for Sonographic Examination of the Fetal Heart: Part 2

The effective performance of fetal cardiac examination using spatiotemporal image correlation (STIC) technology requires 2 essential steps: volume acquisition and postprocessing. An important prerequisite is training sonologists to acquire high-quality volume data sets so that when analyzed, such volumes are informative. This article is part 2 of a series on 4-dimensional sonography with STIC. Part 1 focused on STIC technology and its features, the importance of operator training/experience and acquisition of high-quality STIC volumes, factors that affect STIC volume acquisition rates, and general recommendations on performing 4D sonography with STIC. In part 2, we discuss a detailed and practical stepwise approach for STIC volume acquisition, along with methods to determine whether such volumes are appropriate for analysis.

How to Acquire Cardiac Volumes for Sonographic Examination of the Fetal Heart: Part 1

Four-dimensional sonography with spatiotemporal image correlation (STIC) technology allows acquisition of a fetal cardiac volume data set and displays a cine loop of a complete single cardiac cycle in motion. Part 1 of this 2-part article reviews STIC technology and its features, the importance of operator training/experience, and acquisition of high-quality STIC volumes, as well as factors that affect STIC volume acquisition rates. We also propose a detailed and practical stepwise approach to performing 4-dimensional sonography with STIC and begin herein by providing general recommendations. Part 2 will discuss specifics of the approach, along with how to determine whether such volumes are appropriate for analysis.

Intelligent navigation to improve obstetrical sonography

'Manual navigation' by the operator is the standard method used to obtain information from two-dimensional and volumetric sonography. Two-dimensional sonography is highly operator dependent and requires extensive training and expertise to assess fetal anatomy properly. Most of the sonographic examination time is devoted to acquisition of images, while 'retrieval' and display of diagnostic planes occurs rapidly (essentially instantaneously). In contrast, volumetric sonography has a rapid acquisition phase, but the retrieval and display of relevant diagnostic planes is often time-consuming, tedious and challenging. We propose the term 'intelligent navigation' to refer to a new method of interrogation of a volume dataset whereby identification and selection of key anatomical landmarks allow the system to: 1) generate a geometrical reconstruction of the organ of interest; and 2) automatically navigate, find, extract and display specific diagnostic planes. This is accomplished using operator-independent algorithms that are both predictable and adaptive. Virtual Intelligent Sonographer Assistance (VIS-Assistance®) is a tool that allows operator-independent sonographic navigation and exploration of the surrounding structures in previously identified diagnostic planes. The advantage of intelligent (over manual) navigation in volumetric sonography is the short time required for both acquisition and retrieval and display of diagnostic planes. Intelligent navigation technology automatically realigns the volume, and reorients and standardizes the anatomical position, so that the fetus and the diagnostic planes are consistently displayed in the same manner each time, regardless of the fetal position or the initial orientation. Automatic labeling of anatomical structures, subject orientation and each of the diagnostic planes is also possible. Intelligent navigation technology can operate on conventional computers, and is not dependent on specific ultrasound platforms or on the use of software to perform manual navigation of volume datasets. Diagnostic planes and VIS-Assistance videoclips can be transmitted by telemedicine so that expert consultants can evaluate the images to provide an opinion. The end result is a user-friendly, simple, fast and consistent method of obtaining sonographic images with decreased operator dependency. Intelligent navigation is one approach to improve obstetrical sonography. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.