Intelligent navigation to improve obstetrical sonography

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.

Three-Dimensional Ultrasound for Physical and Virtual Fetal Heart Models: Current Status and Future Perspectives

Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the need for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnoses, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (STIC) was developed in the early 2000s. Some of the advantages of STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed, such as fetal intelligent navigation echocardiography (FINE), also known as "5D heart", in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to diagnose CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled the virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal-fetal interaction in cases of blind pregnant women, and interactive discussions among multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review, the authors discuss advanced image technologies that may optimize prenatal diagnoses of CHDs.

Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees

OBJECTIVE

Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperienced operators. We aimed to develop and validate a prenatal-screening artificial intelligence system (PSAIS) for real-time evaluation of the quality of anatomical images, indicating existing and missing structures.

METHODS

Still ultrasonographic images obtained from fetuses of 18-32 weeks of gestation between 2017 and 2018 were used to develop PSAIS based on YOLOv3 with global (anatomic site) and local (structures) feature extraction that could evaluate the image quality and indicate existing and missing structures in the fetal anatomical images. The performance of the PSAIS in recognizing 19 standard views was evaluated using retrospective real-world fetal scan video validation datasets from four hospitals. We stratified sampled frames (standard, similar-to-standard, and background views at approximately 1:1:1) for experts to blindly verify the results.

RESULTS

The PSAIS was trained using 134 696 images and validated using 836 videos with 12 697 images. For internal and external validations, the multiclass macro-average areas under the receiver operating characteristic curve were 0.943 (95% confidence interval [CI], 0.815-1.000) and 0.958 (0.864-1.000); the micro-average areas were 0.974 (0.970-0.979) and 0.973 (0.965-0.981), respectively. For similar-to-standard views, the PSAIS accurately labeled 90.9% (90.0%-91.4%) with key structures and indicated missing structures.

CONCLUSIONS

An artificial intelligence system developed to assist trainees in fetal anomaly screening demonstrated high agreement with experts in standard view identification.

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.

Use of artificial intelligence in obstetrics: not quite ready for prime time

Artificial intelligence is finding several applications in healthcare settings. This study aimed to report evidence on the effectiveness of artificial intelligence application in obstetrics. Through a narrative review of literature, we described artificial intelligence use in different obstetrical areas as follows: prenatal diagnosis, fetal heart monitoring, prediction and management of pregnancy-related complications (preeclampsia, preterm birth, gestational diabetes mellitus, and placenta accreta spectrum), and labor. Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity. The main advantages that emerged from this review are related to the reduction of inter- and intraoperator variability, time reduction of procedures, and improvement of overall diagnostic performance. However, nowadays, the diffusion of these systems in routine clinical practice raises several issues. Reported evidence is still very limited, and further studies are needed to confirm the clinical applicability of artificial intelligence. Moreover, better training of clinicians designed to use these systems should be ensured, and evidence-based guidelines regarding this topic should be produced to enhance the strengths of artificial systems and minimize their limits.

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.

Optical ultrasound simulation-based training in obstetric sonography

Ultrasound is an imaging modality that is highly operator dependent. This article reviews the challenges in learning how to perform obstetric sonography, as well as the processes necessary to acquire expert performance skills in sonography. Simulation-based education and learning, and the value of medical simulation are also discussed. Ultrasound simulators are an effective means of teaching obstetric sonography, because it provides training, deliberate practice, and performance evaluation/feedback which allows continuous and critical self-evaluation. We review evidence that simulation can improve performance in obstetric ultrasound examination, review current simulators, and discuss the current problems/gaps in ultrasound simulation. Optical positioning ultrasound simulation is a novel high-fidelity simulation learning system, which addresses many of these problems/gaps and is introduced for the first time here.

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.

Artificial Intelligence in Prenatal Ultrasound Diagnosis

The application of artificial intelligence (AI) technology to medical imaging has resulted in great breakthroughs. Given the unique position of ultrasound (US) in prenatal screening, the research on AI in prenatal US has practical significance with its application to prenatal US diagnosis improving work efficiency, providing quantitative assessments, standardizing measurements, improving diagnostic accuracy, and automating image quality control. This review provides an overview of recent studies that have applied AI technology to prenatal US diagnosis and explains the challenges encountered in these applications.

Frontal tangential coronal view two-dimensional ultrasonography in assessment of fetal face [mouth and nose] in comparison with four-dimensional ultrasonography

Background

The fetal face reflects strictly the development of the fetal brain during its growth. Four-dimensional (4D) examination permits continuous monitoring of the examined parts of fetal face and surface. The final performance of obstetric sonographic images depends upon multiple factors, such as fetal lie, uterine wall, abdominal wall fat, amniotic fluid, and the number of gestations which may limit the optimum performance of (4D) ultrasound. The two-dimensional (2D) ultrasound is the first choice due to its wide availability, low cost, and real-time capabilities. The tangential view obtained by (2D) ultrasound coronal sections through the face showed the nose, nostril, lips, eye, lens, and hard palate.

Results

One hundred and sixty fetuses showed straight forwards obstetric examination by both 2D and 4D examinations with identical final reports. While the total number of fetuses with clear images by 2D frontal tangential coronal examination was 191 cases, only 29 cases failed, whereas 170 cases obtained clear images by 4D examination, and 50 cases failed. Both 2D and 4D ultrasound failed to obtain clear images of 19 cases, while 4D failed for 31 cases, and 2D failed for 10 cases. 2D imaging was found to be significantly better than 4D imaging, with a P value of 0.009.

Conclusion

2D ultrasound using the frontal tangential coronal view is an essential part of the fetal examination and more superior than 4D ultrasound in assessing facial anatomy and anomalies, as well comparable to 4D ultrasound as regards fascial expression.

A preliminary study on the prenatal diagnosis of fetal conotruncal defects using intelligent navigation echocardiography

OBJECTIVE

To compare the accuracy, efficiency, and consistency between experienced and less-experienced professionals using intelligent navigation echocardiography.

METHODS

In this prospective study, we enrolled 93 second- and third-trimester fetuses with conotruncal defects (CTD) from July 2017 to February 2018. One or more spatiotemporal image correlation volume data sets were collected per case. The fetuses with CTD were diagnosed by the following two groups of professionals (n = 20 in each) with different experience levels using intelligent navigation echocardiography and two-dimensional ultrasound: group A with 15 years of experience and group B with 1 year of experience. The diagnostic consistency and accuracy of the technologies between the two groups were analyzed.

RESULTS

Satisfactory consistency was noted in the two groups (group A, τ = 0.855, P < 0.05, and group B, τ = 0.821, P < 0.05), and no significant difference in accuracy (χ²  = 3.218, P > 0.05) in using intelligent navigation echocardiography was reported between the two groups. However, there a significant difference in accuracy (χ²  = 0.021, P < 0.05) when using two-dimensional ultrasound was observed between the two groups.

CONCLUSION

Intelligent navigation echocardiography was found to be efficient and accurate for the diagnosis of CTD and good consistency existed in the experienced and less-experienced professionals.

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.

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.

Assessment of the quality of fetal heart standard views using the FAST, STAR, and FINE four-dimensional ultrasound techniques in the screening of congenital heart diseases

OBJECTIVE

To compare the quality of standard fetal echocardiographic views obtained by four-dimensional ultrasound with those obtained by the simple targeted arterial rendering (STAR) technique, four-chamber view swing technique (FAST), and fetal intelligent navigation echocardiography (FINE/5D Heart^® ) technique.

METHODS

This was a cross-sectional prospective study that included pregnant women between 22 and 34 weeks of gestation, with normal fetuses. Fetal heart volumes were acquired using spatio-temporal image correlation (STIC) with the fetal spine between 2 and 9 o'clock. The FAST/STAR techniques consist of the manipulation of STIC volumes by drawing OmniView^™ lines to obtain echocardiographic views. The FINE/5D Heart® technique uses intelligent navigation to automatically generate echocardiographic views. The quality of the images was classified as excellent, good, acceptable, and unacceptable. The analysis was performed using the Bonferroni multiple comparisons test.

RESULTS

The study included 101 pregnant women aged between 16 and 44 years. There was no mean difference in image quality between the techniques regarding fetal spine position in all echocardiographic views (P > .05). However, in the five-chamber, left ventricular outflow tract, right ventricular outflow tract, ductal arch, superior vena cava/inferior vena cava, and abdomen/stomach views, there was a statistically significant mean difference quality between the techniques, regardless of the spine position (P < .05). The best mean image quality was obtained by the FINE technique (P ≤ .016 for all fetal echocardiographic views).

CONCLUSION

The quality of the echocardiographic views obtained using the FINE technique was superior to that of those generated by the FAST/STAR techniques in normal fetuses.

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).

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.

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.

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.

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.

Fetal Intelligent Navigation Echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart

OBJECTIVE

To describe a novel method (Fetal Intelligent Navigation Echocardiography (FINE)) for visualization of standard fetal echocardiography views from volume datasets obtained with spatiotemporal image correlation (STIC) and application of 'intelligent navigation' technology.

METHODS

We developed a method to: 1) demonstrate nine cardiac diagnostic planes; and 2) spontaneously navigate the anatomy surrounding each of the nine cardiac diagnostic planes (Virtual Intelligent Sonographer Assistance (VIS-Assistance®)). The method consists of marking seven anatomical structures of the fetal heart. The following echocardiography views are then automatically generated: 1) four chamber; 2) five chamber; 3) left ventricular outflow tract; 4) short-axis view of great vessels/right ventricular outflow tract; 5) three vessels and trachea; 6) abdomen/stomach; 7) ductal arch; 8) aortic arch; and 9) superior and inferior vena cava. The FINE method was tested in a separate set of 50 STIC volumes of normal hearts (18.6-37.2 weeks of gestation), and visualization rates for fetal echocardiography views using diagnostic planes and/or VIS-Assistance® were calculated. To examine the feasibility of identifying abnormal cardiac anatomy, we tested the method in four cases with proven congenital heart defects (coarctation of aorta, tetralogy of Fallot, transposition of great vessels and pulmonary atresia with intact ventricular septum).

RESULTS

In normal cases, the FINE method was able to generate nine fetal echocardiography views using: 1) diagnostic planes in 78-100% of cases; 2) VIS-Assistance® in 98-100% of cases; and 3) a combination of diagnostic planes and/or VIS-Assistance® in 98-100% of cases. In all four abnormal cases, the FINE method demonstrated evidence of abnormal fetal cardiac anatomy.

CONCLUSIONS

The FINE method can be used to visualize nine standard fetal echocardiography views in normal hearts by applying 'intelligent navigation' technology to STIC volume datasets. This method can simplify examination of the fetal heart and reduce operator dependency. The observation of abnormal echocardiography views in the diagnostic planes and/or VIS-Assistance® should raise the index of suspicion for congenital heart disease.