The effect of increased maternal body habitus on image quality and ability to identify fetal anomalies at a routine 18‐20‐week morphology ultrasound scan: a narrative review

Topography of the Heart: Mapping the Fetal Heart Through SlowflowHD

SlowflowHD is a Doppler Ultrasound modality that is typically geared toward visualization of small-size vessels and low velocity blood flow. In this commentary, we emphasize the importance of implementing the use of SlowflowHD as an adjunct to traditional Doppler modalities in the echocardiography screening in both the first and second trimester. This modality carries many characteristics that allow it to overcome the limitations of our current ultrasound modalities and facilitate mapping of the entirety of the fetal heart. The clinical implications are significant in regard to earlier acquisition of diagnostic information to guide decision-making and patient counseling.

UK obstetric sonographers' experiences of the COVID-19 pandemic: Burnout, role satisfaction and impact on clinical practice

Introduction

The COVID-19 pandemic placed additional demands and stressors on UK obstetric sonographers, who were required to balance parent safety and service quality, alongside staff safety. Increased pressure can negatively impact a healthcare worker's well-being and the provision of person-centred care. The aim of this study was to explore obstetric sonographers' experiences of performing pregnancy ultrasound scans during the pandemic and to assess the impact on burnout, role satisfaction and clinical practice.

Methods

An online, anonymous cross-sectional survey was created to capture sonographers' experience alongside using the Oldenburg Burnout Inventory to evaluate burnout and Clinical Outcomes in Routine Evaluation 10 (CORE-10) to measure psychological distress.

Results

Responses were received from 138 sonographers. Of those completing the Oldenburg Burnout Inventory (n = 89), 92.1% and 91.0% met the burnout thresholds for exhaustion and disengagement, respectively. Sonographers with a higher burnout score also perceived that COVID-19 had a greater, negative impact on their practice (p < 0.05). The mean CORE-10 score of 14.39 (standard deviation = 7.99) suggests mild psychological distress among respondents. A significant decrease in role satisfaction was reported from before to during the pandemic (p < 0.001), which was associated with higher scores for burnout and psychological distress (p < 0.001). Change in role satisfaction was correlated with sonographers' perception of safety while scanning during the pandemic (R ² = 0.148, p < 0.001). Sixty-five sonographers (73.9%) reported they were considering leaving the profession, changing their area of practice or working hours within the next 5 years.

Conclusion

Job and context-specific interventions are required to mitigate burnout and its consequences on the workforce and service provision beyond the pandemic.

APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation

PURPOSE

Artificial-intelligence population-based automated quantification of placental maturation and health from a rapid functional Magnetic Resonance scan. The placenta plays a crucial role for any successful human pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Antenatal assessment in-vivo using T2* relaxometry has shown great promise to inform management and possible interventions but clinical translation is hampered by time consuming manual segmentation and analysis techniques based on comparison against normative curves over gestation.

METHODS

This study proposes a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps: Automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health. These are trained and evaluated on 108 3T MRI placental data sets, the evaluation included 20 high-risk pregnancies diagnosed with pre-eclampsia and/or fetal growth restriction. An independent cohort imaged at 1.5 T is used to assess the generalization of the training and evaluation pipeline.

RESULTS

Across low- and high-risk groups, automatic segmentation performs worse than inter-rater performance (mean Dice coefficients of 0.58 and 0.68, respectively) but is sufficient for estimating placental mean T2* (0.986 Pearson Correlation Coefficient). The placental health prediction achieves an excellent ability to differentiate cases of placental insufficiency between 27 and 33 weeks. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5 T illustrates the ability for direct clinical translation.

CONCLUSION

The presented automatic pipeline facilitates a fast, robust and reliable prediction of placental maturation. It yields human-interpretable and verifiable intermediate results and quantifies uncertainties on the cohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency. APPLAUSE generalizes to an independent cohort imaged at 1.5 T, demonstrating robustness to different operational and clinical environments.