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Segmentations and anatomical landmarks were automated using an in\u2010house 3D deep learning segmentation network, from which cervical 2D measurements and 3D volumes were generated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measures<\/jats:title>\n                    <jats:p>Quality of reconstructed images and segmentations. Inter\u2010rater variability for cervical biometry. Correlation between cervical measurements, maternal demographics and birth outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Successful reconstructions were obtained for 92.9%; 84.9% were good quality. Excellent or good quality segmentations were obtained for all successful reconstructions (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200999). Inter\u2010rater variability between automated and manual biometry was excellent or good for cervical measurements. Total cervical and stroma volumes significantly increased with cervical length (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). Os diameters and utero\u2010cervical angle significantly decreased as cervical length increased (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Cervical stroma volume increased with maternal age (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.02). Controlling for maternal age, an increased cervical volume was associated with an increased risk of caesarean section (OR 1.09,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This is a novel, accurate automated system to assess MRI late gestation cervical biometry and volumetry. We have shown that the late gestation cervical phenotype may influence birth outcomes and provided a new mechanism for increased risk of caesarean with maternal age.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1471-0528.70103","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T09:40:23Z","timestamp":1764754823000},"page":"1166-1173","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low\u2010Field Magnetic Resonance Imaging of the Late Gestation Cervix and Birth Outcome Correlation: A Prospective Cohort Study"],"prefix":"10.1111","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4213-7801","authenticated-orcid":false,"given":"Simi","family":"Bansal","sequence":"first","affiliation":[{"name":"School of Imaging Sciences and Biomedical Engineering, King's College London  London UK"},{"name":"Reseach Department for Early Life Imaging King's College London  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