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Erschienen in: European Radiology 6/2021

14.04.2021 | Imaging Informatics and Artificial Intelligence

Deep learning model for predicting gestational age after the first trimester using fetal MRI

verfasst von: Yasuyuki Kojita, Hidetoshi Matsuo, Tomonori Kanda, Mizuho Nishio, Keitaro Sofue, Munenobu Nogami, Atsushi K. Kono, Masatoshi Hori, Takamichi Murakami

Erschienen in: European Radiology | Ausgabe 6/2021

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Abstract

Objectives

To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD).

Materials and methods

Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin’s concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride’s definition.

Results

The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model’s prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks).

Conclusions

Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester.

Key Points

• The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases.
• Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester.
• Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.
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Metadaten
Titel
Deep learning model for predicting gestational age after the first trimester using fetal MRI
verfasst von
Yasuyuki Kojita
Hidetoshi Matsuo
Tomonori Kanda
Mizuho Nishio
Keitaro Sofue
Munenobu Nogami
Atsushi K. Kono
Masatoshi Hori
Takamichi Murakami
Publikationsdatum
14.04.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 6/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-021-07915-9

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