# Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly

arXiv: https://arxiv.org/abs/2511.07827 · HTML: https://mlsanity.com/paper/2511.07827/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [Segmentation of Muscularis Propria in Colon Histopathology Images Using Vision Transformers for Hirschsprung's Disease](https://mlsanity.com/paper/2412.20571/) (2024)
- [Knowledge-Driven Vision-Language Model for Plexus Detection in Hirschsprung's Disease](https://mlsanity.com/paper/2510.21083/) (2025)

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging](https://mlsanity.com/paper/2512.13434/) (2025)
- [Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning](https://mlsanity.com/paper/2512.22730/) (2025)
- [Automated Classification of First-Trimester Fetal Heart Views Using Ultrasound-Specific Self-Supervised Learning](https://mlsanity.com/paper/2512.24492/) (2025)
- [Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework](https://mlsanity.com/paper/2511.20734/) (2025)
- [Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification](https://mlsanity.com/paper/2602.15339/) (2026)

## Abstract (arXiv)

The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated dataset of fetal brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.