Sam Mathewlynn
BSc (hons) MB ChB MRCOG MFCI
Clinical Research Training Fellow & DPhil Student
- DPhil student
- PI group: Professor Sally Collins
- Consultant obstetrician
I completed my undergraduate training at the University of Bristol before undertaking clinical training in obstetrics and gynaecology in the Thames Valley region. I now work clinically as a consultant obstetrician at the John Radcliffe Hospital, Oxford.
I have in interest in digital health, particularly the development of novel digital tools to assist with risk stratification in the antenatal and postnatal period. I have completed a Topol Fellowship in digital health, which has lead to ongoing work with the National Centre for Maternity Improvement as a Fellow for Future Development, including work on the expansion of the 'Tommy's App'. I have also been admitted by peer review as a member of the Faculty of Clinical Informatics.
My DPhil project will explore the use of 3D ultrasound combined with machine learning techniques to assess the volume and vascularity of the placenta in the first trimester, with the aim of improving the prediction of adverse pregnancy outcomes including pre-eclampsia and fetal growth restriction.
Recent publications
Reference charts for first-trimester placental three-dimensional fractional moving blood volume derived using OxNNet.
Journal article
Mathewlynn S. et al, (2026), Ultrasound Obstet Gynecol, 67, 191 - 200
First-trimester Placental Ultrasound (FirstPLUS) study: prediction of fetal growth restriction using OxNNet-derived first-trimester placental volume.
Journal article
Mathewlynn S. et al, (2026), Ultrasound Obstet Gynecol, 67, 49 - 59
First-trimester biomarkers of gestational diabetes mellitus: A scoping review.
Journal article
Swinburne M. et al, (2025), Acta Obstet Gynecol Scand, 104, 1838 - 1848
Reference charts for first-trimester placental volume derived using OxNNet.
Journal article
Mathewlynn S. et al, (2025), Ultrasound Obstet Gynecol, 66, 337 - 346
From pilot to practice: a scoping review protocol mapping the development of AI-enabled solutions for maternal health using technology readiness levels.
Journal article
Marquardt N. et al, (2025), BMJ Open, 15
Measuring large language model uncertainty in women's health using semantic entropy and perplexity: a comparative study
Journal article
Penny-Dimri JC. et al, (2025)
Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy
Preprint
Penny-Dimri JC. et al, (2025)
Exploring pathophysiological insights to improve diagnostic utility of ultrasound markers for distinguishing placenta accreta spectrum from uterine-scar dehiscence.
Journal article
Adu-Bredu T. et al, (2025), Ultrasound Obstet Gynecol, 65, 85 - 93
ificial Intelligence and Postpartum Hemorrhage.
Journal article
Mathewlynn SJ. et al, (2025), Matern Fetal Med, 7, 22 - 28
Fully automated first trimester placental volume for the prediction of preterm fetal growth restriction: Results of the FirstPLUS study
Conference paper
Mathewlynn S. et al, (2025), BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY, 132, 109 - 109