Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans.

The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a neural network to classify each frame of an ultrasound video recording.

Automated craniofacial biometry with 3D T2w fetal MRI.

Objectives: Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.

Methods: A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI.

Automated Craniofacial Biometry with 3D T2w Fetal MRI.

Objectives: Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated landmark propagation pipeline using 3D motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.

Methods: A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI.

Drug repurposing: identification of SARS-CoV-2 potential inhibitors by virtual screening and pharmacokinetics strategies.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic caused global health, economic, and population loss. Variants of the coronavirus contributed to the severity of the disease and persistent rise in infections. This study aimed to identify potential drug candidates from fifteen approved antiviral drugs against SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike protein (6M0J) using virtual screening and pharmacokinetics to gain insights into COVID-19 therapeutics.

Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs.

Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models.

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.

Coordination of cell cycle and morphogenesis during organ formation.

Organ formation requires precise regulation of cell cycle and morphogenetic events. Using the embryonic salivary gland (SG) as a model, we uncover the role of the SP1/KLF transcription factor Huckebein (Hkb) in coordinating cell cycle regulation and morphogenesis. The mutant SG exhibits defects in invagination positioning and organ size due to the abnormal death of SG cells.

Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

Objectives: Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. Giving additional information about AI model workings, such as confidence scores for AI predictions, may be a way of further improving performance.

Inside the 'imperfect mosaic': Minority ethnic women's qualitative experiences of race and ethnicity during pregnancy, childbirth, and maternity care in the United Kingdom.

Background: Persistent, high rates of maternal mortality amongst ethnic minorities is one of the UK's starkest examples of racial disparity. With greater risks of adverse outcomes during maternity care, ethnic minority women are subjected to embedded, structural and systemic discrimination throughout the healthcare service.

Methods: Fourteen semi-structured interviews were undertaken with minority ethnic women who had recent experience of UK maternity care.

Total and Regional Brain Volumes in Fetuses With Congenital Heart Disease.

Background: Congenital heart disease (CHD) is common and is associated with impaired early brain development and neurodevelopmental outcomes, yet the exact mechanisms underlying these associations are unclear.

Purpose: To utilize MRI data from a cohort of fetuses with CHD as well as typically developing fetuses to test the hypothesis that expected cerebral substrate delivery is associated with total and regional fetal brain volumes.

Study Type: Retrospective case-control study.

Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?

Background: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compare with our own AI model.

Methods: Current screening programme performance was calculated from local and national sources.

Fast fetal head compounding from multi-view 3D ultrasound.

The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy.

A New Objective Radiographic Criteria for Diagnosis of Adult Idiopathic Scoliosis: Apical Pedicle Diameter Asymmetry.

Objective: We sought to test the hypothesis that a difference of ≥1 mm in pedicle diameter between the convex and concave pedicles at the apex of a lumbar curve is a sensitive and/or specific criteria for adult idiopathic scoliosis (AdIS).

Methods: Thirty-nine operative patients with adult deformity and lumbar major curves were identified. A chart review was performed.

3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs.

Exploring the role of a time-efficient MRI assessment of the placenta and fetal brain in uncomplicated pregnancies and these complicated by placental insufficiency.

Introduction: The development of placenta and fetal brain are intricately linked. Placental insufficiency is related to poor neonatal outcomes with impacts on neurodevelopment. This study sought to investigate whether simultaneous fast assessment of placental and fetal brain oxygenation using MRI T2* relaxometry can play a complementary role to US and Doppler US.

Factors which influence ethnic minority women's participation in maternity research: A systematic review of quantitative and qualitative studies.

Background: Women from Black, Asian and mixed ethnicity backgrounds in the UK experience higher rates of maternal and neonatal mortality and morbidity, and report poorer experiences of maternity care. Research is required to understand how to reduce these disparities, however, it is acknowledged these groups of women are under-represented in clinical research.

Aim: To investigate factors which influence participation in maternity research for women from an ethnic minority background.

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.

Background: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking.

PRETUS: A plug-in based platform for real-time ultrasound imaging research.

We present PRETUS - a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brought in via independent plug-ins that can be arranged in sequence. The software allows to capture the real-time stream of ultrasound images from virtually any ultrasound machine, applies computational methods and visualizes the results on-the-fly.

Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g.

Effects of personalized music intervention on nurse burnout: A feasibility randomized controlled trial.

Burnout is highly prevalent among nurses; however, the effect of personalized music intervention on burnout remains unclear on nurses. We aimed to investigate the effects of personalized music intervention in relieving burnout among nurses. Forty-two eligible nurses were recruited for an assessor-blinded, two-arm parallel randomized controlled trial.

Ten-year incidence and treatment outcomes of closed degloving injuries (Morel-Lavallee lesions) in a level 1 trauma centre.

Introduction: Morel-Lavallée lesions (MLL), also referred to as closed degloving injuries, result from traumatic shearing forces with separation of the subcutaneous fat from the underlying fascia. The aim of this study was to determine the incidence and treatment of MLLs at a level 1 trauma centre.

Methods: Single-centre retrospective cross-sectional study of consecutive patients with an imaging diagnosis of a Morel-Lavallee lesion from 1/1/2010-31/12/2019.

UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments.

Introduction: Radiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users.

An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey.

Introduction: As a profession, radiographers have always been keen on adapting and integrating new technologies. The increasing integration of artificial intelligence (AI) into clinical practice in the last five years has been met with scepticism by some, who predict the demise of the profession, whilst others suggest a bright future with AI, full of opportunities and synergies. Post COVID-19 pandemic need for economic recovery and a backlog of medical imaging and reporting may accelerate the adoption of AI.