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

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.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics

BACKGROUND

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care.

OBJECTIVE

The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process.

METHODS

A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion.

RESULTS

According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload.

CONCLUSION

The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

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.

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSION

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

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.

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.