The influence of antenatal imaging on prenatal bonding in uncomplicated pregnancies: a mixed methods analysis.

BACKGROUND: Prenatal bonding describes the emotional connection expectant parents form to their unborn child. Research acknowledges the association between antenatal imaging and enhanced bonding, but the influencing factors are not well understood, particularly for fathers or when using advanced techniques like fetal magnetic resonance imaging (MRI). This study aimed to identify variables which may predict increased bonding after imaging. METHODS: First-time expectant parents (mothers = 58, fathers = 18) completed a two-part questionnaire (QualtricsXM™) about their expectations and experiences of ultrasound (n = 64) or fetal MRI (n = 12) scans in uncomplicated pregnancies. A modified version of the Prenatal Attachment Inventory (PAI) was used to measure bonding. Qualitative data were collected through open-ended questions. Multivariate linear regression models were used to identify significant parent and imaging predictors for bonding. Qualitative content analysis of free-text responses was conducted to further understand the predictors' influences. RESULTS: Bonding scores were significantly increased after imaging for mothers and fathers (p < 0.05). MRI-parents reported significantly higher bonding than ultrasound-parents (p = 0.02). In the first regression model of parent factors (adjusted R2 = 0.17, F = 2.88, p < 0.01), employment status (ß = -0.38, p < 0.05) was a significant predictor for bonding post-imaging. The second model of imaging factors (adjusted R2 = 0.19, F = 3.85, p < 0.01) showed imaging modality (ß = -0.53), imaging experience (ß = 0.42) and parental excitement after the scan (ß = 0.29) were significantly (p < 0.05) associated with increased bonding. Seventeen coded themes were generated from the qualitative content analysis, describing how scans offered reassurance about fetal wellbeing and the opportunity to connect with the baby through quality interactions with imaging professionals. A positive scan experience helped parents to feel excited about parenthood. Fetal MRI was considered a superior modality to ultrasound. CONCLUSIONS: Antenatal imaging provides reassurance of fetal development which affirms parents' emotional investment in the pregnancy and supports the growing connection. Imaging professionals are uniquely positioned to provide parent-centred experiences which may enhance parental excitement and facilitate bonding.

The impact of antenatal imaging on parent experience and prenatal attachment: a systematic review.

INTRODUCTION: Medical imaging in pregnancy (antenatal imaging) is routine. However, the effect of seeing fetal images on the parent-fetal relationship is not well understood, particularly for fathers or partners, or when using advanced imaging technologies. This review aimed to explore how parent experience and prenatal attachment is impacted by antenatal imaging. METHOD: Database searches were performed between September 2020 and April 2021 Inclusion criteria were English language primary research studies published since 2000, describing or reporting measures of attachment after antenatal imaging in expectant parents. The Pillar Integration Process was used for integrative synthesis. FINDINGS: Twenty-three studies were included. Six pillar themes were developed: 1) the scan experience begins before the scan appointment; 2) the scan as a pregnancy ritual; 3) feeling actively involved in the scan; 4) parents' priorities for knowledge and understanding of the scan change during pregnancy; 5) the importance of the parent-sonographer partnership during scanning; and 6) scans help to create a social identity for the unborn baby. CONCLUSION: Antenatal imaging can enhance prenatal attachment. Parents value working collaboratively with sonographers to be actively involved in the experience. Sonographers can help facilitate attachment by delivering parent-centred care tailored to parents' emotional and knowledge needs.

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. AI models were trained using four-chamber ultrasound views of the fetal heart, using a ResNet classifier. RESULTS: Estimated current fetal screening programme sensitivity and specificity for HLHS were 94.3% and 99.985%, respectively. Depending on calibration, AI models to detect HLHS were either highly sensitive (sensitivity 100%, specificity 94.0%) or highly specific (sensitivity 93.3%, specificity 100%). Our analysis suggests that our highly sensitive model would generate 45,134 screen positive results for a gain of 14 additional HLHS cases. Our highly specific model would be associated with two fewer detected HLHS cases, and 118 fewer false positives. CONCLUSION: If used independently, our AI model performance is slightly worse than the performance level of the current screening programme in detecting HLHS, and this performance is likely to deteriorate further when used prospectively. This demonstrates that collaboration between humans and AI will be key for effective future clinical use.

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. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.

The effect of the COVID-19 pandemic on UK parent experiences of pregnancy ultrasound scans and parent-fetal bonding: A mixed methods analysis.

INTRODUCTION: Companionship in antenatal care is important for facilitating positive parental experiences. During the COVID-19 pandemic, restrictions on partner attendance at fetal ultrasound scans were introduced nationally to minimise transmission of the virus. This study aimed to explore the effect of these restrictions on maternal and paternal experiences of pregnancy scans and evaluate their potential effect on parent-fetal bonding. METHODS: A UK-wide, anonymous cross-sectional survey was completed by new and expectant parents (n = 714) who had, or were awaiting a pregnancy scan during the COVID-19 pandemic. The CORE-10 and an adapted version of the Prenatal Attachment Inventory were used to evaluate psychological distress and prenatal bonding. Additional survey questions captured parental experiences of scans. Separate statistical and thematic analyses of the data were undertaken. A joint display matrix was used to facilitate integration of quantitative and qualitative claims to generate a comprehensive interpretation of study findings. FINDINGS: When fathers did not attend the scan, feelings of excitement and satisfaction were significantly reduced (p<0.001) and feelings of anxiety increased (p<0.001) in both parents. Mothers were concerned about receiving unexpected news alone and fathers felt excluded from the scan. Mean paternal bonding (38.22, SD 10.73) was significantly lower compared to mothers (47.01, SD 7.67) although no difference was demonstrated between those who had attended the scan and those who had not. CORE-10 scores suggested low-to-mild levels of psychological distress, although the mean difference between mothers and fathers was not significant. Key themes described both parents' sense of loss for their desired pregnancy scan experience and reflected on sonographers' central role in providing parent-centred care during scans. CONCLUSION: Restrictions on partner attendance at scans during the COVID-19 pandemic had a negative effect on parental experiences of antenatal imaging. Provision of parent-centred care, which is inclusive of partners, is essential for improved parental experiences.

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.

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. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS: Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS: The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.

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. METHODS: A systematic review was conducted to examine influencing factors for research participation. MEDLINE/CINHAL/PsycInfo/EMBASE databases were systematically searched in March 2021 and updated in March 2022. Papers were eligible if they explored maternal research participation and identified a woman's ethnicity in the results. No restrictions were placed on methodology. A convergent integrated approach was used to synthesise findings. FINDINGS: A total of 14 papers met the inclusion criteria. Results were divided into eight overarching themes. A personalised approach to recruitment and incorporating culturally sensitive communication and considerations enhanced research participation. Distrust around sharing data, a perception of risk to research participation, and research lacking in personal relevance adversely affected the decision to participate. Large variation existed in the quality of the studies reviewed. CONCLUSIONS: Consideration of a woman's culture and background in the design and the delivery of a maternity research study may facilitate participation, particularly when sampling from a specific population. Further research, informed by women from ethnic minority backgrounds is warranted to develop women-centred recommendations for conducting inclusive maternity research. Prospero registration: www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42021261686.

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., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.

The impact of the COVID-19 pandemic on clinical guidance and risk assessments, and the importance of effective leadership to support UK obstetric sonographers.

INTRODUCTION: The COVID-19 pandemic had a profound impact on the provision of obstetric ultrasound services, leading to the publication of new guidance and requirement for individual departmental risk assessments in the UK. The impact of these changes on clinical practice for UK obstetric sonographers is not currently well reported in published literature. METHODS: Obstetric sonographers working in the UK (n = 138) used the Qualtrics XMTM platform to complete an anonymous, online questionnaire about their experiences during the pandemic. Participants responded to closed-type questions about national guidance, risk assessment and their perception of support, and provided additional detail about their experiences in these areas through free-text response options. RESULTS: Over 90% of respondents were aware of or had read guidance issued by professional organisations, although challenges for its implementation in departments were identified. These were commonly related to the clinical working environment and included limitations on physical space (76.3%), time constraints (67.5%) and ventilation (61.3%). Sonographers felt most supported by their ultrasound colleagues (83.5%) and line managers (41.2%). They felt least supported by senior management and leadership personnel (60.8%), other antenatal colleagues (51.5%) and professional organisations (41.2%). CONCLUSION: Obstetric sonographers will need support from the wider service team and professional organisations to facilitate post-pandemic recovery of the workforce. Formal clinical supervision programmes may be beneficial in facilitating a more holistic approach to peer-support, although there is currently limited evidence of their use in sonographic practice.

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. It is therefore timely to appreciate practitioners' perceptions of AI used in clinical practice and their perception of the short-term impact on the profession. AIM: This study aims to explore the perceptions of AI in the UK radiography workforce and to investigate its current AI applications and future technological expectations of radiographers. METHODS: An online survey (QualtricsⓇ) was created by a team of radiography AI experts. The survey was disseminated via social media and professional networks in the UK. Demographic information and perceptions of the impact of AI on several aspects of the radiography profession were gathered, including the current use of AI in practice, future expectations and the perceived impact of AI on the profession. RESULTS: 411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)). Awareness of AI used in clinical practice is low, with DR respondents suggesting AI will have the most value/potential in cross sectional imaging and image reporting. TR responses linked AI as having most value in treatment planning, contouring, and image acquisition/matching. Respondents felt that AI will impact radiographers' daily work (DR, 79.6%; TR, 88.9%) by standardising some aspects of patient care and technical factors of radiography practice. A mixed response about impact on careers was reported. CONCLUSIONS: Respondents were unsure about the ways in which AI is currently used in practice and how AI will impact on careers in the future. It was felt that AI integration will lead to increased job opportunities to contribute to decision making as an end user. Job security was not identified as a cause for concern.

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools. METHODS: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. RESULTS: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. CONCLUSION: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.

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. Plug-ins can run concurrently without blocking each other. They can be implemented in C++ and Python. A graphical user interface can be implemented for each plug-in, and presented to the user in a compact way. The software is free and open source, and allows for rapid prototyping and testing of real-time ultrasound imaging methods in a manufacturer-agnostic fashion. The software is provided with input, output and processing plug-ins, as well as with tutorials to illustrate how to develop new plug-ins for PRETUS.

Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers.

Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.

MRI-Derived Fetal Weight Estimation in the Midpregnancy Fetus: A Method Comparison Study.

OBJECTIVES: The aim of this study was to compare the standard ultrasound (US) estimated fetal weight (EFW) and MRI volume-derived methods for the midtrimester fetus. METHODS: Twenty-five paired US and MRI scans had the EFW calculated (gestational age [GA] range = 20-26 weeks). The intra- and interobserver variability of each method was assessed (2 operators/modality). A small sub-analysis was performed on 5 fetuses who were delivered preterm (mean GA 29 +3 weeks) and compared to the actual birthweight. RESULTS: Two MRI volumetry EFW formulae under-measured compared to US by -10.9% and -14.5% in the midpregnancy fetus (p < 0.001) but had excellent intra- and interobserver agreement (intraclass correlation coefficient = 0.998 and 0.993). In the preterm fetus, the mean relative difference (MRD) between the MRI volume-derived EFW (MRI-EFW) and actual expected birthweight (at the scan GA) was -13.7% (-159.0 g, 95% CI: -341.7 to 23.7 g) and -17.1% (-204.6 g, 95% CI: -380.4 to -28.8 g), for the 2 MRI formulae. The MRD was smaller for US at 5.3% (69.8 g, 95% CI: -34.3 to 173.9). CONCLUSIONS: MRI-EFW results should be interpreted with caution in midpregnancy. Despite excellent observer agreement with MRI volumetry, refinement of the EFW formula is needed in the second trimester, for the small and for the GA and preterm fetus to compensate for lower fetal densities.

Towards Standardized Acquisition with a Dual-probe Ultrasound Robot for Fetal Imaging.

Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way.

Electronic consenting for conducting research remotely: A review of current practice and key recommendations for using e-consenting.

BACKGROUND: Electronic approaches are becoming more widely used to obtain informed consent for research participation. Electronic consent (e-consent) provides an accessible and versatile approach to the consenting process, which can be enhanced with audio-visual and interactive features to improve participant engagement and comprehension of study procedures. Best practice guidance underpinned by ethical principles is required to ensure effective implementation of e-consent for use in research. AIM: To identify the key considerations for successful and ethical implementation of e-consent in the recruitment of participants to research projects which are conducted remotely. METHODS: Electronic database searches of CINAHL, Medline, Embase, DARE, HTA, PubMed, the Cochrane Library, Scopus, Web of Science, NHS Evidence, and hand-searches of reference lists were performed. Primary research studies of adult (≥ 18 years old) research participants using e-consent, published in English language, peer-reviewed journals between 2010-2020 were eligible for inclusion. RESULTS: Of the initial 665 identified studies, 18 met the inclusion criteria: 6 cohort studies, 5 qualitative studies, 4 randomised control trials, 2 mixed-methods studies and one case-control study. Critical appraisal of included studies using Critical Appraisal Skills Program (CASP) tools suggested a low to moderate risk of bias in most studies (n = 15). Key practice recommendations for researchers using e-consent were identified around five primary themes: 1) accessibility and user-friendliness of e-consent, 2) user engagement and comprehension, 3) customisability to participant preferences and demographics, 4) data security and 5) impact on research teams. CONCLUSION: E-consenting approaches are generally well received by participants, with most studies reporting user-friendly interfaces and sufficient participant comprehension of consenting documentation. IMPLICATIONS FOR PRACTICE: E-consent may facilitate remotely-conducted research by offering a feasible and robust alternative to face-to-face consenting approaches, however paper-based options should still be offered, based on participant preference. Customising e-consenting platforms may improve accessibility for individuals with specific needs, and increase engagement with study information. Research teams must offer prospective participants opportunities to discuss study information in real-time.