# Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images

arXiv: https://arxiv.org/abs/2201.07935 · HTML: https://mlsanity.com/paper/2201.07935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 177 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1804.09102/) (2018)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2007.15273/) (2020)
- [Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning](https://mlsanity.com/paper/1909.00273/) (2019)
- [Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers](https://mlsanity.com/paper/1908.02582/) (2019)
- [Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2103.14502/) (2021)
- [Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound](https://mlsanity.com/paper/2004.00207/) (2020)
- [Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening](https://mlsanity.com/paper/2008.06966/) (2020)
- [Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks](https://mlsanity.com/paper/1911.00908/) (2019)
- [Weakly Supervised Localisation for Fetal Ultrasound Images](https://mlsanity.com/paper/1808.00793/) (2018)
- [Discovering Salient Anatomical Landmarks by Predicting Human Gaze](https://mlsanity.com/paper/2001.08188/) (2020)
- [Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes](https://mlsanity.com/paper/2004.13567/) (2020)
- [Image Segmentation Using Hybrid Representations](https://mlsanity.com/paper/2004.07071/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI](https://mlsanity.com/paper/2307.04573/) (2023)

## Abstract (arXiv)

Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM Library, Google Scholar, and the Web of Science. We retrieved studies published between 2010 to 2021. Data extracted from studies were synthesized using a narrative approach. Out of 1269 retrieved studies, we included 107 distinct studies from queries that were relevant to the topic in the survey. We found that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound images (n=19). Classification is the most used method (n=42), followed by segmentation (n=31), classification integrated with segmentation (n=16) and other miscellaneous such as object-detection, regression and reinforcement learning (n=18). The most common areas within the pregnancy domain were the fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen (n=10), and lastly the fetus face (n=10). In the most recent studies, deep learning techniques were primarily used (n=81), followed by machine learning (n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI techniques played a crucial role in predicting fetal diseases and identifying fetus anatomy structures during pregnancy. More research is required to validate this technology from a physician's perspective, such as pilot studies and randomized controlled trials on AI and its applications in a hospital setting.