Neonatal and fetal brain pathology

Últimos artículos

• Neonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder

  Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody · 28 de julio de 2026

  In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals. By…

• Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning

  Sujith K Mandala · 7 de julio de 2026

  Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. However, it remains challenging due to data complexity and limited labeled samples. This research paper presents a novel machine-learning approach for fetal health…

• Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI

  Diego Fajardo-Rojas, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter · 19 de junio de 2026

  Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to…

• GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

  Boyu Yuan, Jiamiao Lu, Weichuan Zhang, Benqing Wu, Tuo Wang, Changshan Wang, Changming Sun, Liang Guo · 2 de junio de 2026

  This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightweight 3D CNN based on ResNet-10, pretrained on MedicalNet to address data scarcity. A global manifold mapping strategy fir…

• FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting

  Kjersti Engan, Neel Kanwal, Anita Yeconia, Ladislaus Blacy, Yuda Munyaw, Estomih Mduma, Hege Ersdal · 29 de mayo de 2026

  Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in assessing fetal well-being during prenatal care, enabling the detection of abnormal patterns and supporting timely obstet…

• Artificial Intelligence-Assistant Cardiotocography: Unified Model for Signal Reconstruction, Fetal Heart Rate Analysis, and Variability Assessment

  Xiaohua Wang, Kai Yu, XuXiao Liang, Liang Wang, Chao Han · 15 de mayo de 2026

  The monitoring of fetal heart rate (FHR) and the assessment of its variability are crucial for preventing fetal compromise and adverse outcomes. However, traditional methods encounter limitations arising from equipment performance, data transmission, and subjective assessments by doctors. We have de…

• PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL

  Sheng Wong, Ravi Shankar, Beth Albert, Hao Fei, Lin Li, Imane Ben M'Barek, Manu Vatish, Gabriel Davis Jones · 6 de mayo de 2026

  Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically informative clinical recordings untapped. To address this limitation, we propose Physiology-aware Rep…

• Honest and Reliable Evaluation and Expert Equivalence Testing of Automated Neonatal Seizure Detection

  Jovana Kljajic, John M. O'Toole, Robert Hogan, Tamara Skoric · 6 de marzo de 2026

  Reliable evaluation of machine learning models for neonatal seizure detection is critical for clinical adoption. Current practices often rely on inconsistent and biased metrics, hindering model comparability and interpretability. Expert-level claims about AI performance are frequently made without r…

• Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

  Martin G. Frasch, Marlene J. E. Mayer, Clara Becker, Peter Zimmermann, Camilla Zelgert, Marta C. Antonelli, Silvia M. Lobmaier · 5 de febrero de 2026

  Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental outcomes. Current screening relies on subjective questionnaires (PSS-10), limiting continuous monitoring. We developed deep learning models for stress de…

• Deep learning-based neurodevelopmental assessment in preterm infants

  Lexin Ren, Jiamiao Lu, Weichuan Zhang, Benqing Wu, Tuo Wang, Yi Liao, Jiapan Guo, Changming Sun, Liang Guo · 20 de enero de 2026

  Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for timely intervention. While deep learning-based volumetric segmentation of brain MRI scans offers a promising avenue for assessing neonatal neurodevelo…

• Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification

  Weitao Tang, Johann Vargas-Calixto, Nasim Katebi, Robert Galinsky, Gari D. Clifford, Faezeh Marzbanrad · 19 de enero de 2026

  Study Objectives: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first…

• A Foundation Model Approach for Fetal Stress Prediction During Labor From cardiotocography (CTG) recordings

  Naomi Fridman, Berta Ben Shachar · 13 de enero de 2026

  Intrapartum cardiotocography (CTG) is widely used for fetal monitoring during labor, yet its interpretation suffers from high inter-observer variability and limited predictive accuracy. Deep learning approaches have been constrained by the scarcity of CTG recordings with clinical outcome labels. We …

• A Patient-Independent Neonatal Seizure Prediction Model Using Reduced Montage EEG and ECG

  Sithmini Ranasingha, Agasthi Haputhanthri, Hansa Marasinghe, Nima Wickramasinghe, Kithmin Wickremasinghe, Jithangi Wanigasinghe, Chamira U. S. Edussooriya, Joshua P. Kulasingham · 19 de noviembre de 2025

  Neonates are highly susceptible to seizures, often leading to short or long-term neurological impairments. However, clinical manifestations of neonatal seizures are subtle and often lead to misdiagnoses. This increases the risk of prolonged, untreated seizure activity and subsequent brain injury. Co…

• Large language models surpass domain-specific architectures for antepartum electronic fetal monitoring analysis

  Sheng Wong, Ravi Shankar, Beth Albert, Gabriel Davis Jones · 7 de noviembre de 2025

  Foundation models (FMs) and large language models (LLMs) have demonstrated promising generalization across diverse domains for time-series analysis, yet their potential for electronic fetal monitoring (EFM) and cardiotocography (CTG) analysis remains underexplored. Most existing CTG studies relied o…

• Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy

  Fabio Magarelli, Geraldine B. Boylan, Saeed Montazeri, Feargal O'Sullivan, Dominic Lightbody, Minoo Ashoori, Tamara Skoric, John M. O'Toole · 31 de octubre de 2025

  Machine learning (ML) has the potential to support and improve expert performance in monitoring the brain function of at-risk newborns. Developing accurate and reliable ML models depends on access to high-quality, annotated data, a resource in short supply. ML competitions address this need by provi…