Items where City Author is "Skelton, Emily"
Stogiannos, N. ORCID: 0000-0003-1378-6631, Skelton, E.
ORCID: 0000-0003-0132-7948, Kumar, S.
ORCID: 0009-0009-6443-1217 (2025).
Evaluation of a customised, AI-focused educational seminar delivered to final year undergraduate radiography students in the UK: A cross-sectional study.
Radiography, 31(3),
article number 102926.
doi: 10.1016/j.radi.2025.102926
Ngo, M. ORCID: 0000-0003-0377-1460, Thorburn, K., Naama, A. (2025).
Exploring the lived experiences of diagnostic radiographers after transitioning to non-emergency imaging settings.
Radiography, 31(2),
article number 102871.
doi: 10.1016/j.radi.2025.01.006
Butterfield, E. & Skelton, E. ORCID: 0000-0003-0132-7948 (2024).
The effect of an enhanced fetal growth ultrasound protocol on pregnancy outcomes: A retrospective service evaluation within a single UK National Health Service centre between 2014 and 2022.
Ultrasound,
doi: 10.1177/1742271x241287925
Skelton, E. ORCID: 0000-0003-0132-7948, Cromb, D., Smith, A. (2024).
The influence of antenatal imaging on prenatal bonding in uncomplicated pregnancies: a mixed methods analysis.
BMC Pregnancy and Childbirth, 265(1),
article number 265.
doi: 10.1186/s12884-024-06469-0
Skelton, E. ORCID: 0000-0003-0132-7948, Cromb, D., Smith, A. (2024).
"It's not just the medical aspects that are important": A qualitative exploration of first-time parents' experiences of antenatal imaging and their influence on parent-fetal bonding.
Radiography, 30(1),
pp. 288-295.
doi: 10.1016/j.radi.2023.11.019
Day, T. G., Budd, S., Tan, J. (2023). Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?. Prenatal Diagnosis, 44(6-7), pp. 717-724. doi: 10.1002/pd.6445
Skelton, E. ORCID: 0000-0003-0132-7948, Smith, A., Harrison, G.
ORCID: 0000-0003-2795-8190 (2023).
The effect of the COVID-19 pandemic on UK parent experiences of pregnancy ultrasound scans and parent-fetal bonding: A mixed methods analysis.
PLoS ONE, 18(6),
article number e0286578.
doi: 10.1371/journal.pone.0286578
Skelton, E. ORCID: 0000-0003-0132-7948, Smith, A., Harrison, G. (2023).
"It has been the most difficult time in my career": A qualitative exploration of UK obstetric sonographers' experiences during the COVID-19 pandemic.
Radiography, 29(3),
pp. 582-589.
doi: 10.1016/j.radi.2023.03.007
Lovell, H., Silverio, S. A., Story, L. (2023). Factors which influence ethnic minority women’s participation in maternity research: A systematic review of quantitative and qualitative studies. PLoS ONE, 18(2), article number e0282088. doi: 10.1371/journal.pone.0282088
van de Venter, R., Skelton, E. ORCID: 0000-0003-0132-7948, Matthew, J. (2023).
Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.
Insights Imaging, 14(1),
article number 25.
doi: 10.1186/s13244-023-01372-2
Zimmer, V. A., Gomez, A., Skelton, E. ORCID: 0000-0003-0132-7948 (2023).
Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.
Medical Image Analysis, 83,
article number 102639.
doi: 10.1016/j.media.2022.102639
Skelton, E. ORCID: 0000-0003-0132-7948, Malamateniou, C.
ORCID: 0000-0002-2352-8575 & Harrison, G.
ORCID: 0000-0003-2795-8190 (2022).
The impact of the COVID-19 pandemic on clinical guidance and risk assessments, and the importance of effective leadership to support UK obstetric sonographers.
Journal of Medical Imaging and Radiation Sciences, 53(4),
S107-S115.
doi: 10.1016/j.jmir.2022.10.003
Rainey, C., O'Regan, T., Matthew, J. (2022). UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments. Radiography, 28(4), pp. 881-888. doi: 10.1016/j.radi.2022.06.006
Stogiannos, N. ORCID: 0000-0003-1378-6631, Skelton, E.
ORCID: 0000-0003-0132-7948, Rogers, C.
ORCID: 0000-0001-7820-7309 (2022).
Leadership and resilience in adversity: The impact of COVID-19 on radiography researchers and ways forward.
Journal of Medical Imaging and Radiation Sciences, 53(4),
S47-S52.
doi: 10.1016/j.jmir.2022.09.011
Skelton, E. ORCID: 0000-0003-0132-7948, Webb, R.
ORCID: 0000-0002-8862-6491, Malamateniou, C.
ORCID: 0000-0002-2352-8575 (2022).
The impact of antenatal imaging in pregnancy on parent experience and prenatal attachment: a systematic review.
Journal of Reproductive and Infant Psychology, 42(1),
pp. 22-44.
doi: 10.1080/02646838.2022.2088710
Rainey, C., O'Regan, T., Matthew, J. (2022). An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey. Journal of Medical Imaging and Radiation Sciences, 53(3), pp. 347-361. doi: 10.1016/j.jmir.2022.05.010
Skelton, E. ORCID: 0000-0003-0132-7948, Harrison, G.
ORCID: 0000-0003-2795-8190, Rutherford, M. (2022).
UK obstetric sonographers’ experiences of the COVID-19 pandemic: Burnout, role satisfaction and impact on clinical practice.
Ultrasound, 31(1),
pp. 12-22.
doi: 10.1177/1742271x221091716
Gomez, A., Zimmer, V. A., Wheeler, G. (2022). PRETUS: A plug-in based platform for real-time ultrasound imaging research. SoftwareX, 17, article number 100959. doi: 10.1016/j.softx.2021.100959
Matthew, Jacqueline, Skelton, Emily ORCID: 0000-0003-0132-7948, Day, Thomas G (2022).
Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.
Prenatal Diagnosis, 42(1),
pp. 49-59.
doi: 10.1002/pd.6059
Matthew, J., Skelton, E. ORCID: 0000-0003-0132-7948, Story, L. (2021).
MRI-Derived Fetal Weight Estimation in the Midpregnancy Fetus: A Method Comparison Study.
Fetal Diagnosis and Therapy, 48(10),
pp. 708-719.
doi: 10.1159/000519115
Malamateniou, C. ORCID: 0000-0002-2352-8575, McFadden, S., McQuinlan, Y. (2021).
Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group.
Radiography, 27(4),
pp. 1192-1202.
doi: 10.1016/j.radi.2021.07.028
Skelton, E. ORCID: 0000-0003-0132-7948, Matthew, J., Li, Y. (2020).
Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison.
Radiography, 27(2),
pp. 519-526.
doi: 10.1016/j.radi.2020.11.006