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Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 47642

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Special Issue Editors

Dr. Aimée Lovers

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Guest Editor

Department of Obstetrics and Gynecology, Amsterdam University Medical Center, Amsterdam, The Netherlands
Interests: fetal monitoring; fetal (patho)physiology; big data; clinical decision support

Dr. Giulia Baldazzi

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Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, CA 09124, Italy
Interests: biomedical signal processing; machine learning; non-invasive fetal ECG; cardiac electrophysiology; neural signal processing; epidermal electronics

Dr. Nicolò Pini

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Guest Editor

Columbia University Irving Medical Center, New York, NY 10032, USA
Interests: fetal monitoring; non-invasive fetal ECG; wearable electronics; remote monitoring; clinical decision support; low-middle income settings; developmental neuroscience

Dr. Danilo Pani

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, CA 09124, Italy
Interests: biomedical signal processing; fetal electrocardiography; wearable electronics; cardiac electrophysiology; neural engineering; real-time processing
Special Issues, Collections and Topics in MDPI journals

Dr. Antoniya Georgieva

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Guest Editor

John Radcliffe Hospital, L3 Womens Ctr, Oxford OX3 9DU, UK
Interests: big maternity data; clinical decision support; fetal monitoring; AI; data science

Prof. Dr. Patrice Abry

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Guest Editor

Physics Laboratory, CNRS UMR 5672, ENS Lyon, Lyon, France
Interests: statistical signal processing; scale-free dynamics; fractal; optimization; learning

Dr. Martin Gerbert Frasch

E-Mail Website
Guest Editor

Center on Human Development and Disability, University of Washington, Seattle, WA 98195-6460, USA
Interests: digital health; health monitoring; wearables; outcome prediction; development; physiology; neuroscience; AI/ML

Special Issue Information

Dear Colleagues,

Over the past two decades, several improvements in technologies for fetal and neonatal health have been achieved. However, no significant reductions in stillbirths, neonatal deaths, severe brain injuries from hypoxic-ischemic events, and cardiovascular diseases occurred. On the one hand, the complex pathogenesis of perinatal mortality, neonatal brain injury, and congenital heart diseases hamper our understanding of the underpinning of such important aspects. On the other hand, no disruptive technology has emerged, which means that significant advancement in perinatal diagnosis, monitoring, and treatment is still desired. Remarkably, better detection and prevention are needed to advance obstetric and neonatal care around the world. Overall, this requires a multidisciplinary approach across the entire pregnancy care pathway, incorporating perspectives from researchers, clinicians, medical device manufacturers, software developers, and other relevant stakeholders such as policy makers and patient communities.

The aim of this Special Issue of Bioengineering is to represent the research, along with the associated challenges and opportunities, for innovative methods and technologies for fetal-maternal monitoring, diagnostics and therapeutics during pregnancy, labor, and delivery. We are excited to provide a view from both academic institutions and the industry spanning all stakeholders, from both researchers and device/algorithm developers as well as the voices of the clinical care providers and the patients.

Topics covered will include but are not limited to,

advances in the physiological and clinical understanding of fetal development and pathogenesis of perinatal mortality
fetal heart monitoring using cardiotocography and maternal abdominal electrocardiogram
fetal neurodevelopment
new medical devices for perinatal care
signal processing techniques
artificial intelligence applications
regulatory aspects
challenges in the development of large-scale predictive models of fetal compromise throughout the entire pregnancy.

Dr. Aimée Lovers
Dr. Giulia Baldazzi
Dr. Nicolò Pini
Dr. Danilo Pani
Dr. Antoniya Georgieva
Prof. Dr. Patrice Abry
Dr. Martin Gerbert Frasch
Guest Editors

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Keywords

fetal monitoring
perinatal medicine
antenatal care
pregnancy
signal processing
artificial intelligence
simulators

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Published Papers (12 papers)

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Research

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18 pages, 341 KB

Open AccessArticle

Identifying High-Risk Pre-Term Pregnancies Using the Fetal Heart Rate and Machine Learning

by Gabriel Davis Jones, William R. Cooke and Manu Vatish

Bioengineering 2026, 13(2), 203; https://doi.org/10.3390/bioengineering13020203 - 11 Feb 2026

Viewed by 412

Abstract

Fetal heart rate (FHR) monitoring is ubiquitous in antenatal care, yet human visual interpretation poorly predicts adverse pregnancy outcomes. Meanwhile, preterm gestations carry a high burden of stillbirth and severe fetal compromise, where earlier identification of high-risk pregnancies may justify iatrogenic preterm delivery to prevent avoidable fetal death. We analyzed 4867 antepartum FHR recordings from pre-term pregnancies meeting at least one of ten adverse outcome criteria alongside 4014 term uncomplicated controls. Seven clinically validated FHR features were extracted from each trace, and six machine-learning classifiers were trained on 80% of the data (7105 samples) using k-fold cross-validation; the remaining 20% (1776 samples) formed an internal validation cohort. The random forest demonstrated the best performance, achieving an area under the receiver-operating characteristic curve (AUC) of 0.88 (95% confidence interval [CI] 0.87–0.88) during training and 0.88 (95% CI 0.86–0.90) on validation, with good calibration (Brier score 0.14). Median AUC across individual adverse outcomes was 0.85 (interquartile range [IQR] 0.81–0.89) and exceeded 0.80 at all gestational ages assessed; sensitivity and specificity at the Youden threshold were 76.2% and 87.5%, respectively. Decision-curve analysis demonstrated net benefit across a range of clinically relevant probability thresholds. These findings indicate that data-driven interpretation of antepartum FHR can stratify risk in pre-term pregnancies with high accuracy and may support earlier, evidence-based clinical decision-making, particularly in resource-limited settings where specialist expertise is limited. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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17 pages, 6410 KB

Open AccessArticle

IESS-FusionNet: Physiologically Inspired EEG-EMG Fusion with Linear Recurrent Attention for Infantile Epileptic Spasms Syndrome Detection

by Junyuan Feng, Zhenzhen Liu, Linlin Shen, Xiaoling Luo, Yan Chen, Lin Li and Tian Zhang

Bioengineering 2026, 13(1), 57; https://doi.org/10.3390/bioengineering13010057 - 31 Dec 2025

Viewed by 768

Abstract

Infantile Epileptic Spasms Syndrome (IESS) is a devastating epileptic encephalopathy of infancy that carries a high risk of lifelong neurodevelopmental disability. Timely diagnosis is critical, as every week of delay in effective treatment is associated with worse cognitive outcomes. Although synchronized electroencephalogram (EEG) and surface electromyography (EMG) recordings capture both the electrophysiological and motor signatures of spasms, accurate automated detection remains challenging due to the non-stationary nature of the signals and the absence of physiologically plausible inter-modal fusion in current deep learning approaches. We introduce IESS-FusionNet, an end-to-end dual-stream framework specifically designed for accurate, real-time IESS detection from simultaneous EEG and EMG. Each modality is processed by a dedicated Unimodal Encoder that hierarchically integrates Continuous Wavelet Transform, Spatio-Temporal Convolution, and Bidirectional Mamba to efficiently extract frequency-specific, spatially structured, local and long-range temporal features within a compact module. A novel Cross Time-Mixing module, built upon the linear recurrent attention of the Receptance Weighted Key Value (RWKV) architecture, subsequently performs efficient, time-decaying, bidirectional cross-modal integration that explicitly respects the causal and physiological properties of cortico-muscular coupling during spasms. Evaluated on an in-house clinical dataset of synchronized EEG-EMG recordings from infants with confirmed IESS, IESS-FusionNet achieves 89.5% accuracy, 90.7% specificity, and 88.3% sensitivity, significantly outperforming recent unimodal and multimodal baselines. Comprehensive ablation studies validate the contribution of each component, while the proposed cross-modal fusion requires approximately 60% fewer parameters than equivalent quadratic cross-attention mechanisms, making it suitable for real-time clinical deployment. IESS-FusionNet delivers an accurate, computationally efficient solution with physiologically inspired cross-modal fusion for the automated detection of infantile epileptic spasms, offering promise for future clinical applications in reducing diagnostic delay. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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17 pages, 2554 KB

Open AccessArticle

Evaluating Hemodynamic Changes in Preterm Infants Using Recent YOLO Models

by Li-Cheng Huang, Zi-Wei Zheng, Ming-Chih Lin and Yu-Ting Tsai

Bioengineering 2025, 12(8), 815; https://doi.org/10.3390/bioengineering12080815 - 29 Jul 2025

Viewed by 849

Abstract

This research aims to offer a deep learning-based diagnostic approach for hemorrhagic complications linked to patent ductus arteriosus (PDA) in preterm infants. Utilizing the You Only Look Once (YOLO) algorithm, this research analyzed five key cardiac parameters derived from echocardiographic ultrasonic waves: the left ventricular ejection time (LVET), left ventricular internal dimension at diastole (LVIDd), left ventricular internal dimension at systole (LVIDs), posterior wall thickness at end-systole (HES), and RR interval between two successive R-waves. The proposed ensemble model achieved best-in-class detection accuracies for each parameter, with rates of 97.56% (LVET), 88.69% (LVIDd), 99.50% (LVIDs), 82.29% (HES), and 81.15% (RR interval). Furthermore, assessment of cardiac function using derived indices—end-systolic wall stress (ESWS) and rate-corrected mean velocity of circumferential fiber shortening (mVcfc)—achieved mean accuracy rates of 82.33% and 90.16%, respectively. This approach enables physicians to accurately evaluate cardiac function in preterm infants and facilitates the diagnosis of PDA-related hemorrhagic complications. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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23 pages, 2768 KB

Open AccessArticle

Deep Learning for Generalized EEG Seizure Detection after Hypoxia–Ischemia—Preclinical Validation

by Hamid Abbasi, Joanne O. Davidson, Simerdeep K. Dhillon, Kelly Q. Zhou, Guido Wassink, Alistair J. Gunn and Laura Bennet

Bioengineering 2024, 11(3), 217; https://doi.org/10.3390/bioengineering11030217 - 24 Feb 2024

Cited by 2 | Viewed by 3106

Abstract

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia–ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI–normothermia term (n = 7), HI–hypothermia term (n = 14), sham–normothermia term (n = 5), and HI–normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI–normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI–normothermia preterm data were used in training, the performance on HI–normothermia term and HI–hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI–normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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15 pages, 892 KB

Open AccessArticle

Data-Driven Insights into Labor Progression with Gaussian Processes

by Tilekbek Zhoroev, Emily F. Hamilton and Philip A. Warrick

Bioengineering 2024, 11(1), 73; https://doi.org/10.3390/bioengineering11010073 - 11 Jan 2024

Cited by 1 | Viewed by 2141

Abstract

Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of labor progress, suitable for real-time use, that predicts cervical dilation and fetal station based on clinically relevant predictors available from the pelvic exam and cardiotocography. We show that the model is more accurate than a statistical approach using a mixed-effects model. In addition, it provides confidence estimates on the prediction, calibrated to the specific delivery. Finally, we show that predicting both dilation and station with a single Gaussian process model is more accurate than two separate models with single predictions. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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19 pages, 1757 KB

Open AccessArticle

Prediction of Fetal Blood Pressure during Labour with Deep Learning Techniques

by John Tolladay, Christopher A. Lear, Laura Bennet, Alistair J. Gunn and Antoniya Georgieva

Bioengineering 2023, 10(7), 775; https://doi.org/10.3390/bioengineering10070775 - 28 Jun 2023

Cited by 3 | Viewed by 2386

Abstract

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional neural network. This model was then used to estimate FBP for decelerations extracted from the final 90 min of 53,445 human FHR signals collected using cardiotocography. Minimum sheep FBP was predicted with a mean absolute error of

6.7

mmHg (25th, 50th, 75th percentiles of

2.3

5.2

9.7

mmHg), mean absolute percentage errors of

17.3

% (

5.5

12.5

23.9

%) and a coefficient of determination

R^{2} = 0.36

. While the model was unable to clearly predict severe compromise at birth in humans, there is positive evidence that such a model could predict human FBP with further development. The neural network is capable of predicting FBP for many of the sheep decelerations accurately but performed far from satisfactory at identifying FHR segments that correspond to the highest or lowest minimum FBP. These results indicate that with further work and a larger, more variable training dataset, the model could achieve higher accuracy. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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17 pages, 2837 KB

Open AccessArticle

Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data

by Daniel Asfaw, Ivan Jordanov, Lawrence Impey, Ana Namburete, Raymond Lee and Antoniya Georgieva

Bioengineering 2023, 10(6), 730; https://doi.org/10.3390/bioengineering10060730 - 19 Jun 2023

Cited by 14 | Viewed by 4229

Abstract

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0–10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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Review

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45 pages, 1232 KB

Open AccessReview

Predicting Intrapartum Acidemia: A Review of Approaches Based on Fetal Heart Rate

by Gabriele Varisco, Giulio Steyde, Elisabetta Peri, Iris Hoogendoorn, Maria G. Signorini, Judith O. E. H. van Laar, Massimo Mischi and Marieke B. van der Hout-van der Jagt

Bioengineering 2026, 13(2), 146; https://doi.org/10.3390/bioengineering13020146 - 27 Jan 2026

Viewed by 602

Abstract

Fetal acidemia, caused by impaired gas exchange between the fetus and the mother, is a leading cause of stillbirth and neurologic complications. Early prediction is therefore essential to guide timely clinical intervention. Several strategies rely on cardiotocography (CTG), which combines fetal heart rate (fHR) with uterine contractions and has led to development of clinical guidelines for CTG interpretation and the introduction of different fHR features. Additionally, ST event analysis, investigating changes in the ST segments of the fetal electrocardiogram (fECG), has been proposed as a complementary tool. This narrative review adopts a systematic approach, with comprehensive searches in Embase and PubMed to ensure full coverage of the available literature, and summarizes findings from 30 studies. Clinical guidelines for CTG interpretation frequently lead to intermediate risk level annotations, leaving the final decision regarding fetal management to clinical experience. In contrast, various fHR features can successfully discriminate between fetuses developing acidemia and healthy controls. Evidence regarding the added value of ST events derived from the scalp electrode remains conflicting, due to concerns about invasiveness. Recent studies on machine learning models highlight their ability to integrate multiple fHR features and improve predictive performance, suggesting a promising direction for enhancing acidemia prediction during labor. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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20 pages, 1971 KB

Open AccessReview

Fetal Heart Rate Preprocessing Techniques: A Scoping Review

by Inês Campos, Hernâni Gonçalves, João Bernardes and Luísa Castro

Bioengineering 2024, 11(4), 368; https://doi.org/10.3390/bioengineering11040368 - 11 Apr 2024

Cited by 5 | Viewed by 5634

Abstract

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, describes the preprocessing methods in original research articles on human FHR (or beat-to-beat intervals) signal preprocessing from PubMed and Web of Science, published from their inception up to May 2021. From the 322 unique articles identified, 54 were included, from which prevalent preprocessing approaches were identified, primarily focusing on the detection and correction of poor signal quality events. Detection usually entailed analyzing deviations from neighboring samples, whereas correction often relied on interpolation techniques. It was also noted that there is a lack of consensus regarding the definition of missing samples, outliers, and artifacts. Trends indicate a surge in research interest in the decade 2011–2021. This review underscores the need for standardizing FHR signal preprocessing techniques to enhance diagnostic accuracy. Future work should focus on applying and evaluating these methods across FHR databases aiming to assess their effectiveness and propose improvements. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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28 pages, 5375 KB

Open AccessReview

Acquisition Devices for Fetal Phonocardiography: A Scoping Review

by Noemi Giordano, Agnese Sbrollini, Micaela Morettini, Samanta Rosati, Gabriella Balestra, Ennio Gambi, Marco Knaflitz and Laura Burattini

Bioengineering 2024, 11(4), 367; https://doi.org/10.3390/bioengineering11040367 - 11 Apr 2024

Cited by 2 | Viewed by 4279

Abstract

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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28 pages, 1174 KB

Open AccessReview

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

by Lochana Mendis, Marimuthu Palaniswami, Fiona Brownfoot and Emerson Keenan

Bioengineering 2023, 10(9), 1007; https://doi.org/10.3390/bioengineering10091007 - 25 Aug 2023

Cited by 32 | Viewed by 15085

Abstract

The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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16 pages, 1699 KB

Open AccessReview

Heart Rate Variability Code: Does It Exist and Can We Hack It?

by Martin Gerbert Frasch

Bioengineering 2023, 10(7), 822; https://doi.org/10.3390/bioengineering10070822 - 10 Jul 2023

Cited by 9 | Viewed by 3344

Abstract

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal–analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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