{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T07:23:24Z","timestamp":1787642604862,"version":"build-2736575974"},"reference-count":81,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR300555"],"award-info":[{"award-number":["NIHR300555"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:sec id=\"sec042\">\n<jats:title>Objectives<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec043\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29\u201336 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec044\">\n<jats:title>Results<\/jats:title>\n<jats:p>Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p&lt;0.001). Manual measurements took 25\u201335 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19\u201339 weeks), based on 280 control fetuses, were produced for future research.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec045\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pdig.0000663","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T18:22:22Z","timestamp":1735582942000},"page":"e0000663","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated craniofacial biometry with 3D T2w fetal MRI"],"prefix":"10.1371","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-0322","authenticated-orcid":true,"given":"Jacqueline","family":"Matthew","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alena","family":"Uus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexia","family":"Egloff 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