Dataset Information

Prenatal Diagnosis of Lissencephaly Type 2 using Three-dimensional Ultrasound and Fetal MRI: Case Report and Review of the Literature.

ABSTRACT: Lissencephaly is a genetic heterogeneous autosomal recessive disorder characterized by the classical triad: brain malformations, eye anomalies, and congenital muscular dystrophy. Prenatal diagnosis is feasible by demonstrating abnormal development of sulci and gyri. Magnetic resonance imaging (MRI) may enhance detection of developmental cortical disorders as well as ocular anomalies. We describe a case of early diagnosis of lissencephaly type 2 detected at the time of routine second trimester scan by three-dimensional ultrasound and fetal MRI. Gross pathology confirmed the accuracy of the prenatal diagnosis while histology showed the typical feature of cobblestone cortex. As the disease is associated with poor perinatal prognosis, early and accurate prenatal diagnosis is important for genetic counseling and antenatal care.

SUBMITTER: Tonni G

PROVIDER: S-EPMC10309364 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Publications

Prenatal Diagnosis of Lissencephaly Type 2 using Three-dimensional Ultrasound and Fetal MRI: Case Report and Review of the Literature.

Tonni Gabriele G Pattacini Pierpaolo P Bonasoni Maria Paola MP Araujo Júnior Edward E

Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia 20160418 4

Lissencephaly is a genetic heterogeneous autosomal recessive disorder characterized by the classical triad: brain malformations, eye anomalies, and congenital muscular dystrophy. Prenatal diagnosis is feasible by demonstrating abnormal development of sulci and gyri. Magnetic resonance imaging (MRI) may enhance detection of developmental cortical disorders as well as ocular anomalies. We describe a case of early diagnosis of lissencephaly type 2 detected at the time of routine second trimester sc ...[more]

PMID: 27088705

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Project description:The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Transabdominal ultrasound can be challenging due to the small fetal brain and its movement, as well as multiple sweeps that may not yield high-quality images, especially when brain structures are unclear. By applying the latest developments in artificial intelligence for automated image processing allowing automated training of brain anatomy in these images retrieving reliable quantitative brain measurements becomes possible at a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound. We applied the trained model in a large longitudinal population sample from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate biological sex differences in fetal ICV as a proof-of-principle and validation for our automated method (N = 2235 individuals with 43492 ultrasounds). A total of 168 annotated, randomly selected, good quality 3D ultrasound whole-brain images were included to train a 3D CNN for automated fetal ICV segmentation. A data augmentation strategy provided physical variation to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV (Dice Similarity Coefficient (DSC) > 0.93, Hausdorff Distance (HD): HDvoxel < 4.6 voxels, and HDphysical < 1.4 mm). In addition, we developed an automated quality control procedure to include the ultrasound scans that successfully predicted ICV from all 43492 3D ultrasounds available in all individuals, no longer requiring manual selection of the best scan for analysis. Our trained model automatically retrieved ultrasounds with brain data and estimated ICV and ICV growth in 7672 (18%) of ultrasounds in 1762 participants that passed the automatic quality control procedure. Boys had significantly larger ICV at 20-weeks (81.7 ± 0.4 mL vs. 80.8 ± 0.5 mL; B = 2.86; p = 5.7e-14) and 30-weeks (257.0 ± 0.9 mL vs. 245.1 ± 0.9 mL; B = 12.35; p = 8.2e-27) of pregnancy, and more pronounced ICV growth than girls (delta growth 0.12 mL/day; p = 1.8e-5). Our automated artificial intelligence approach provides an opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

Dataset Information

Prenatal Diagnosis of Lissencephaly Type 2 using Three-dimensional Ultrasound and Fetal MRI: Case Report and Review of the Literature.

Publications

Prenatal Diagnosis of Lissencephaly Type 2 using Three-dimensional Ultrasound and Fetal MRI: Case Report and Review of the Literature.

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