# The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence

arXiv: https://arxiv.org/abs/2302.00225 · HTML: https://mlsanity.com/paper/2302.00225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 239 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks](https://mlsanity.com/paper/1710.02615/) (2017)
- [Federated Learning of Generative Image Priors for MRI Reconstruction](https://mlsanity.com/paper/2202.04175/) (2022)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022)
- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging](https://mlsanity.com/paper/2107.02375/) (2021)
- [Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease](https://mlsanity.com/paper/1803.05192/) (2018)
- [Capsules for Biomedical Image Segmentation](https://mlsanity.com/paper/2004.04736/) (2020)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning](https://mlsanity.com/paper/1704.08797/) (2017)
- [Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches](https://mlsanity.com/paper/1801.03230/) (2018)
- [Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks](https://mlsanity.com/paper/1907.05708/) (2019)
- [Automatically Designing CNN Architectures for Medical Image Segmentation](https://mlsanity.com/paper/1807.07663/) (2018)
- [Transformer based Generative Adversarial Network for Liver Segmentation](https://mlsanity.com/paper/2205.10663/) (2022)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [Multi-Contrast MRI Segmentation Trained on Synthetic Images](https://mlsanity.com/paper/2207.02469/) (2022)
- [Multi-feature classifiers for burst detection in single EEG channels from preterm infants](https://mlsanity.com/paper/1702.02873/) (2017)
- [Classification of Neurodevelopmental Age in Normal Infants Using 3D-CNN based on Brain MRI](https://mlsanity.com/paper/1910.12159/) (2019)

## Built on by (most cited first, 2 of 2)

- [Pediatric Pancreas Segmentation from MRI Scans with Deep Learning](https://mlsanity.com/paper/2506.15908/) (2025)
- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)

## Abstract (arXiv)

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.