Cross-sectional reference values of cerebral ventricle for Chinese neonates born at 25–41 weeks of gestation

Peng, Yulin; Huang, Boyang; Luo, Yingchun; Huang, Xiaoliang; Yao, Longmei; Shi, Zujin

doi:10.1007/s00431-022-04547-z

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…We also exploited the intra-ICCs to evaluate the intra-rater reliability between the ultrasonographers and inter-ICCs to assess the inter-rater reliability between the ultrasonographers and algorithms. The ultrasonographers’ intra-ICCs and inter-ICCs were graded good reliability or higher, which is common in well-designed and well-executed research 37 . The suggested algorithm was graded moderate or good reliability, higher than the others.…”

Section: Discussionmentioning

confidence: 92%

“…The intra-ICCs were calculated with the absolute agreement, single measures, and two-way mixed effect model; similarly, the inter-ICCs were determined using the absolute agreement, single measures, and two-way random effect model. Finally, the point and interval estimations of ICCs were reported in accordance with the guidelines 37 , 48 , 49 .…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, all statistical analyses were conducted with the two-tailed test at a significant level of 0.05 without adjustment for multiple testing 37 .…”

Section: Methodsmentioning

confidence: 99%

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Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans

Peng,

Luo,

Yan

et al. 2024

Sci Rep

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This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group. The root mean square error, explained variation, and mean absolute percentage error (MAPE) were utilized for the validity assessment; the two-sample t test, Mann–Whitney U test, Wilcoxon signed-rank test, Bland–Altman plot, and intraclass correlation coefficient (ICC) for the reliability evaluation. Our suggested algorithm outperformed all the competitors in all groups and indices regarding validity, except for the MAPE, where the Inception-v3 surpassed ours in the NPG. The two-sample t test and Mann–Whitney U test indicated no significant difference between the suggested method and the gold standard in group-level comparison. The Wilcoxon signed-rank test revealed significant differences between our new approach and the gold standard in personal-level comparison. All points in Bland–Altman plots fell between the upper and lower limits of agreement. The inter-ICCs of ultrasonographers, our proposed algorithm, and its opponents were graded good reliability, good or moderate reliability, and moderate or poor reliability, respectively. Our proposed approach surpasses the competition and is as reliable as manual measurement.

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Section: Discussionmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

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Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans

Peng,

Luo,

Yan

et al. 2024

Sci Rep

Self Cite

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High‐level feature‐guided attention optimized neural network for neonatal lateral ventricular dilatation prediction

Peng,

Liu,

Deng

et al. 2024

Medical Physics

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BackgroundPeriventricular‐intraventricular hemorrhage can lead to posthemorrhagic ventricular dilatation or even posthemorrhagic hydrocephalus if not detected promptly. Sequential cranial ultrasound scans are typically used for their diagnoses. Nonetheless, manual image audit has numerous disadvantages.PurposeThis study aimed to develop a predictive model utilizing modified inception (MI) and high‐level feature‐guided attention (HFA) modules for predicting neonatal lateral ventricular dilation via ultrasound images.MethodsThe MI modules reduced input data sizes and dimensions, while the HFA modules effectively delved into semantic information through supervision from high‐level feature images to low‐level feature images. The process facilitated the accurate identification of dilated lateral ventricles. A total of 710 neonates, corresponding to 1420 lateral ventricles, were recruited in this study. Each lateral ventricle was captured in two images, one on the parasagittal plane and the other on the coronal plane. The combination of anterior horn width, ventricular index, thalamo‐occipital distance, and ventricular height served as the gold standard. A lateral ventricle would be considered dilatated if any of these four indices exceeded its upper reference value. These lateral ventricles were randomly split into training and testing sets at a 7:3 ratio. We evaluated the validity of our proposed approach and its competitors across the coronal plane, parasagittal plane, and overall performance. We also determined the impact of subjects’ baseline characteristics on the overall performance of the proposed approach. Additionally, ablation analyses were conducted to ensure the efficacy of the proposed approach.ResultsOur proposed approach achieved the largest Youden index (0.65, 95% CI: 0.58–0.72), DOR (27.11, 95% CI: 15.89–46.26), area under curves (AUC) of receiver operating characteristic curve (ROC) (0.84, 95% CI: 0.80–0.88), and AUC of precision‐recall curve (PRC) (0.81, 95% CI: 0.74–0.86) in the overall performance assessment and ablation analyses. Moreover, it boasted the biggest Cramer's V values on the coronal (Cramer's V = 0.488, p < 0.001) and parasagittal (Cramer's V = 0.713, p < 0.001) planes individually. Factors such as left side, male sex, singleton birth, and vaginal delivery were positively correlated with higher performance regarding the proposed algorithm, except for the gestational age.ConclusionThis work provides a novel attention optimized algorithm for rapid and accurate ventricular dilatation predictions. It surpasses the traditional algorithms in terms of validity whether concerning the coronal plane, parasagittal plane, or overall performance. The overall performance of algorithms will be influenced by the baseline characteristics of populations.

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Attention mechanism optimized neural network for automatic measurement of fetal anterior-neck-lower-jaw angle in nuchal translucency tests

Peng

Zeng

Luo

et al. 2023

Multimed Tools Appl

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Cross-sectional reference values of cerebral ventricle for Chinese neonates born at 25–41 weeks of gestation

Cited by 8 publications

References 26 publications

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans

High‐level feature‐guided attention optimized neural network for neonatal lateral ventricular dilatation prediction

Attention mechanism optimized neural network for automatic measurement of fetal anterior-neck-lower-jaw angle in nuchal translucency tests

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