Evidence map›Paper›PMID 42700234›Full record

ArticlePediatric radiology2026

Automated fetal magnetic resonance imaging lung segmentation for rapid lung volume estimation across diverse pulmonary hypoplasia phenotypes.

Jungwhan John Choi, Shruti Hegde, Jonathan Dudley, Laura A Galganski, Beth M Kline-Fath, Elanchezhian Somasundaram, Jonathan R Dillman

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Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jungwhan John ChoiDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA. jungwhan.choi@cchmc.org.ORCID https://orcid.org/0000-0002-1682-7833
Shruti HegdeDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
Jonathan DudleyDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.ORCID https://orcid.org/0000-0002-5189-9568
Laura A GalganskiUniversity of Cincinnati, College of Medicine, Cincinnati, OH, USA.
Beth M Kline-FathDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
Elanchezhian SomasundaramDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.ORCID https://orcid.org/0000-0002-0440-5238
Jonathan R DillmanDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.ORCID https://orcid.org/0000-0003-0124-0164

Funding

Cincinnati Children's Hospital Medical Center Radiology Department Pilot Grant GR900136-004
6 · The paper itself

Abstract

backgroundFetal magnetic resonance imaging (MRI)-derived lung volume measurements are used for prenatal risk stratification in conditions associated with pulmonary hypoplasia, but manual segmentation is time-intensive.

objectiveTo evaluate whether automated fetal MRI lung segmentation enables rapid and reliable volumetric assessment across diverse pulmonary hypoplasia phenotypes. MATERIALS AND

methodsIn this retrospective study, fetal MRI examinations performed from 2016 to 2025 with expert-adjudicated lung segmentations obtained during clinical care were used for model development and testing. Automated segmentation pipelines were developed for total lung volume (TLV) and percent predicted lung volume (PPLV). Performance was assessed using Dice similarity coefficient (DSC), absolute percentage error (APE), Bland-Altman analysis, intraclass correlation coefficient (ICC), case-level risk-category agreement, and inference time. Lowest-performing TLV outputs were adjudicated by a fetal radiologist to simulate a human-in-the-loop workflow.

resultsThe final dataset included 906 fetal MRI examinations with complete and recoverable lung segmentations, acquired at a mean gestational age of 27.1 weeks, range 16-38 weeks. Automated TLV estimation showed excellent agreement with manual volumetry (ICC, 0.955; mean DSC, 0.81±0.10; median APE 11.1%). Automated PPLV estimation showed lower performance (ICC, 0.727; mean DSC, 0.76±0.12; median APE 14.5%). Mean inference time was 4 s per examination. Correction of the 25 lowest-performing TLV outputs required 3.1 min per case.

conclusionAutomated fetal MRI lung segmentation enables rapid TLV estimation across diverse pulmonary hypoplasia phenotypes and may improve clinical workflow efficiency while preserving expert oversight for challenging cases.

Indexed as

Artificial intelligenceCongenital diaphragmatic herniaDeep learningFetusImage segmentationLungMagnetic resonance imagingPulmonary hypoplasia

Identifiers

PMID42700234

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.