A "holistic" sonographic view on congenital heart disease: How automatic reconstruction using fetal intelligent navigation echocardiography eases unveiling of abnormal cardiac anatomy part II-Left heart anomalies

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

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

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.

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.

Assessment of left ventricular function by spatio-temporal image correlation in fetuses with fetal growth restriction

AIM

To compare the evaluation of left ventricular function by spatio-temporal image correlation (STIC) between fetal growth restriction (FGR) fetuses and normal fetuses.

METHODS

Forty-two FGR fetuses and 50 normal fetuses with gestational age ranging from 28 to 35 weeks, were chosen for the study group and control group, respectively. The fetal heart was acquired using the STIC modality, beginning with a four-chamber view. A 7.5-12.5 s acquisition time and 20-35°angle of the acquisition were used for the acquisition. The resulting STIC dataset was saved for offline analysis. Ventricular volumes were measured using the Virtual Organ Computer-aided Analysis (VOCAL) mode, where the observer defines the contours of the ventricle and traces the endocardia. Stroke volume (SV) = end diastolic volume (EDV)-end systolic volume (ESV) and ejection fraction (EF) = SV/EDV × 100%. The data of the two groups were analyzed.

RESULTS

(1) SV increased with fetal growth in both groups and was positively correlated with gestational age (p < .01), whereas EF remained constant throughout gestation and had no correlation with gestational age (p > .05). (2) There was no difference found in EF between the two groups, (p > .05), SV was significantly lower in FGR group than those in the normal group (p < .01).

CONCLUSION

The STIC is a precise method for calculating fetal ventricular volume changes and functions. Reduced SV occurred at the initial stage of fetal deterioration before the discovery of abnormal EF in FGR fetuses, indicating cardiac dysfunction. SV could be a sensitive indicator of cardiac dysfunction. The use of EF to assess fetal cardiac function is not perfect.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.