The January issue of Ultrasound in Obstetrics & Gynecology includes a systematic review exploring the value of whole-genome sequencing in the diagnosis of congenital anomalies, a randomized controlled trial investigating the utility of visual biofeedback during the second stage of labor in facilitating maternal pushing, a study on the diagnosis of superficial endometriosis using transvaginal ultrasound, and a study demonstrating the potential of deep learning in improving fetal heart screening. This issue also features the new ISUOG Practice Guidelines on the performance of third-trimester obstetric ultrasound scan.
Please see below a selection of articles from the January issue of the Journal chosen specially by the UOG team. To view all UOG content, become an ISUOG member today or login and upgrade.
Incremental yield of whole-genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta-analysis
Whole-genome sequencing (WGS) enables assessment of the entire genome and theoretically should offer additional diagnostic capability compared with other genetic testing techniques. In this systematic review and meta-analysis, Shreeve et al. investigate the diagnostic performance of WGS compared with the sequential approach of QF-PCR/chromosomal microarray analysis and exome sequencing in cases with congenital malformations in the perinatal period or during infancy. Their review demonstrates that there is presently no evidence of a significant advantage in terms of diagnostic yield using WGS compared with the stepwise testing strategy (incremental yield of 1%). However, WGS appears to require less DNA and may provide results in a shorter timeframe to facilitate decision-making. Although there is currently insufficient evidence to endorse the use of WGS over exome sequencing, the authors believe that the diagnostic yield of WGS will likely improve with advances in post-sequencing analysis and interpretation of results.
Visual biofeedback for shortening second stage of labor: randomized controlled trial
Visual biofeedback has been proposed as a complementary tool to facilitate maternal pushing during the second stage of labor in women under epidural anesthesia. This study by Preuss et al. aimed to verify the effectiveness of this strategy in a setting of a randomized controlled trial. The findings of the study suggest that pushing in the second stage of labor may be more effective with the aid of visual biofeedback, demonstrated by a higher angle of progression in the biofeedback group vs the control group. However, the study shows no significant effect of visual biofeedback on the length of the second stage of labor, possibly due to the short duration of the intervention (up to 10 min in most cases). The authors postulate that visual biofeedback may facilitate descent of the fetal head during maternal pushing, and future studies should investigate the effect of a continuous intervention on the duration of the second stage of labor.
Diagnosis of superficial endometriosis on transvaginal ultrasound by visualization of peritoneum of pouch of Douglas
Around 80% of women with endometriosis have superficial endometriosis. However, to date, advances in non-invasive, imaging-based diagnosis have been limited to ovarian or deep endometriosis. In this study, Bailey et al. demonstrate that direct visualization of superficial lesions of endometriosis in the peritoneum of the pouch of Douglas on routine transvaginal ultrasound can diagnose superficial endometriosis with high specificity. While negative transvaginal ultrasound does not reliably confirm the absence of disease or replace diagnostic laparoscopy, positive transvaginal ultrasound may facilitate a non-invasive diagnosis for a much larger group of women than was previously possible and reduce the risk, cost and delay associated with a surgical diagnosis.
Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection
It has been suggested that as many as 70% of cases of congenital heart disease are missed prenatally, which may result in severe morbidity and death. Standardization and automation of approaches to prenatal screening are therefore needed to improve lesion detection. In this study, Athalye et al. assess the performance of a previously developed deep-learning model using fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. According to their findings, the deep-learning model has higher sensitivity compared with clinical detection (91% vs 53%) and performs well in detecting congenital heart defects in community-acquired images in a low-risk population, including lesions to which it had not been exposed previously. These findings support the proposition that deep-learning models can improve prenatal detection of congenital heart disease.
Coming up next month…
- A systematic review on the outcome of selective fetal growth restriction in dichorionic diamniotic twin pregnancy. Preview the Accepted Article.
- A prospective study on the diagnostic accuracy of a standardized transvaginal ultrasound approach for deep endometriosis of the uterosacral ligaments and torus uterinus. Preview the Accepted Article.
- A study presenting novel reference charts for fetal growth in twins. Preview the Accepted Article.