Prenatal Diagnosis of Isolated Single Umbilical Artery: Incidence, Risk Factors and Impact on Pregnancy Outcomes

Deep learning for fetal inflammatory response diagnosis in the umbilical cord

INTRODUCTION

Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI).

METHODS

We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin (H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble.

RESULTS

The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the ensembled predictions using ConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from top accuracy model appropriately highlighted arteritis in cases of FIR 2. In FIR 1, the highest performing model assigned high attention to areas of activated-appearing stroma in Wharton's Jelly. However, other high-performing models assigned attention to umbilical vessels.

DISCUSSION

We developed models for diagnosis of FIR from placental histology images, helping reduce interobserver variability among pathologists. Future work may examine the utility of these models for identifying infants at risk of systemic inflammatory response or early onset neonatal sepsis.

Fetal Isolated Single Umbilical Artery (ISUA) and Its Role as a Marker of Adverse Perinatal Outcomes

Single umbilical artery (SUA) is considered an ultrasound marker of anomalies. Although it may be present in about 0.5% to 6% of normal pregnancies, it has been linked with an increased risk of fetal growth restriction (FGR), as well as cardiac, genitourinary and gastrointestinal malformations and chromosomal anomalies such as trisomies 21 and 18. Objectives: This study aims to evaluate whether the presence of isolated SUA (ISUA) is associated with adverse perinatal outcomes. Methods: A descriptive, observational and retrospective study was conducted, analyzing 1234 pregnancies (1157 normal gestations with a three-vessel cord and 77 cases of ISUA). Results: ISUA was associated with a lower gestational age (38 vs. 39 weeks) and a lower birth weight (3013 vs. 3183 g) when performing a univariate analysis. However, after performing a multivariate analysis adjusted for maternal age and BMI, the association between single umbilical artery (SUA) and lower birth weight could not be proven. No significant differences were found in the rate of malformations, genetic disorders, Apgar score, pH at birth or admissions in the neonatal ICU. Conclusions: ISUA is associated with a lower birth weight but does not increase the risk of prematurity or low-birth-weight-related neonatal admissions. Additionally, ISUA is not significantly associated with a lower gestational age, genetic disorders, fetal malformations, worse Apgar scores or lower pH values at birth.

Prenatal Sonographic Features of Noonan Syndrome: Case Series and Literature Review

Noonan syndro me is a rare autosomal dominant congenital abnormality associated with a gene defect located on the short arm of chromosome 12. It is characterized by dysmorphic facies, webbed neck, short stature, lymphatic obstruction, cardiac anomalies, and intellectual disability. Prenatal diagnosis of Noonan syndrome is rare because there are no pathognomonic sonographic signs. Studies on the prenatal sonographic features of Noonan syndrome have been reported in very limited numbers. This case series of severe fetal Noonan syndrome, together with a literature review, was conducted to establish prenatal sonographic features highly suggestive of Noonan syndrome to facilitate early detection by clinicians. This study reveals that Noonan syndrome has a relatively specific pattern, which facilitates prenatal molecular genetic diagnosis. Increased nuchal translucency (NT) in the late first trimester and fluid collection in the early second trimester could be warning signs for follow-up, prompting further investigation to detect late-onset features and leading to molecular genetic confirmation. Most structural abnormalities appear in the second trimester, with progressive changes noted throughout gestation. This review better characterizes the sonographic features of fetal Noonan syndrome based on a larger sample size, illustrating a wider spectrum of prenatal phenotypes, including lymphatic drainage disorders, cardiac abnormalities, polyhydramnios, and absent ductus venosus.

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies

OBJECTIVE

The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models.

METHODS

Ultrasound images obtained retrospectively were analyzed for blinding, text removal, AI training, and image prediction. After developing and testing text removal methods, a small n-size study (40 images) using fastai/PyTorch to classify umbilical cord images. This data set was expanded to 286 lateral-CFI images that were used to compare: different neural network performance, diagnostic value, and model predictions.

RESULTS

AI-Optical Character Recognition method was superior in its ability to remove text from images. The small n-size mixed single umbilical artery determination data set was tested with a pretrained ResNet34 neural network and obtained and error rate average of 0.083 (n = 3). The expanded data set was then tested with several AI models. The majority of the tested networks were able to obtain an average error rate of <0.15 with minimal modifications. The ResNet34-default performed the best with: an image-classification error rate of 0.0175, sensitivity of 1.00, specificity of 0.97, and ability to correctly infer classification.

CONCLUSION

This work provides a robust framework for ultrasound image AI classifications. AI could successfully classify umbilical cord types of ultrasound image study with excellent diagnostic value. Together this study provides a reproducible framework to develop AI-specific ultrasound classification of umbilical cord or other diagnoses to be used in conjunction with physicians for optimal patient care.