A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound

Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges

PURPOSE

This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this physiologically continuous spectrum of mother and child health, as well as to highlight potential pitfalls, and suggest solutions for the same.

METHOD

A systematic search identified studies employing AI techniques for prediction, diagnosis, and decision support employing various modalities like imaging, electrophysiological signals and electronic health records in the domain of obstetrics and fetal health. In the selected articles then, AI applications in fetal morphology, gestational age assessment, congenital defect detection, fetal monitoring, placental analysis, and maternal physiological monitoring were critically examined both from the perspective of the domain and artificial intelligence.

RESULT

AI-driven solutions demonstrate promising capabilities in medical diagnostics and risk prediction, offering automation, improved accuracy, and the potential for personalized medicine. However, challenges regarding data availability, algorithmic transparency, and ethical considerations must be overcome to ensure responsible and effective clinical implementation. These challenges must be urgently addressed to ensure a domain as critical to public health as obstetrics and fetal health, is able to fully benefit from the gigantic strides made in the field of artificial intelligence.

CONCLUSION

Open access to relevant datasets is crucial for equitable progress in this critical public health domain. Integrating responsible and explainable AI, while addressing ethical considerations, is essential to maximize the public health benefits of AI-driven solutions in maternal-fetal care.

A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

BACKGROUND

Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches.

OBJECTIVES

This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations.

METHODOLOGY

In order to ensure transparency and rigor, the authors followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion and exclusion criteria, a final set of 25 articles was selected.

OUTCOMES

The findings revealed significant advancements in AI-powered DS diagnostics across diverse data modalities. The modalities, including facial images, ultrasound scans, and genetic data, demonstrated strong potential for early DS diagnosis. Despite these advancements, this review outlined the limitations of AI approaches. Small and imbalanced datasets reduce the generalizability of the AI models. The authors present actionable strategies to enhance the clinical adoptions of these models.

[Artificial intelligence for the comprehensive approach to orphan/rare diseases: A scoping review]

INTRODUCTION

Orphan diseases (OD) are rare but collectively common, presenting challenges such as late diagnoses, disease progression, and limited therapeutic options. Recently, artificial intelligence (AI) has gained interest in the research of these diseases.

OBJECTIVE

To synthesize the available evidence on the use of AI in the comprehensive approach to orphan diseases.

METHODS

An exploratory systematic review of the Scoping Review type was conducted in PubMed, Bireme, and Scopus from 2019 to 2024.

RESULTS

fifty-six articles were identified, with 21.4% being experimental studies; 28 documents did not specify an OD, 8 documents focused primarily on genetic diseases; 53.57% focused on diagnosis, and 36 different algorithms were identified.

CONCLUSIONS

The information found shows the development of AI algorithms in different clinical settings, confirming the potential benefits in diagnosis times, therapeutic options, and greater awareness among health professionals.

3D ultrasound evaluation of fetal ears in prenatal syndrome diagnosis - a comparative study

PURPOSE

The aim of the study was to assess fetal ears on prenatal 3D ultrasound and compare ear surface patterns and measurements between fetuses with syndromes and healthy fetuses.

MATERIALS AND METHODS

Our study is based on 3D ultrasound images of 100 fetuses between the 20th and 37th week of gestation. We compared 50 ears of fetuses with syndromes (syndrome group) to 50 gestational age-matched ears of healthy fetuses (control group). The syndrome group consisted of fetuses with Trisomy 21 (n=13), Trisomy 18 (n=9) and other syndromes (n=28). The evaluation was based on measuring the ear length and width as well as developing categories to describe and compare different ear surface anomalies.

RESULTS

Ears of fetuses with Trisomy 18 were on average 0.423 cm smaller in length (P<0.001) and 0.123 cm smaller in width (P=0.031) and grew on average 0.046 cm less in length per week of gestation (P=0.027) than those of healthy fetuses. Ears of fetuses with Trisomy 21 differed from healthy fetuses regarding the form of the helix (P=0.013) and the ratio of the concha to the auricle (P=0.037). Fetuses with syndromes demonstrated less ear surface details than their controls (syndrome group: P=0.018, P=0.005; other syndromes subgroup: P=0.020). We saw an increased richness of ear surface details at a later gestational age both in the fetuses with syndromes and the healthy fetuses.

CONCLUSION

Ears of fetuses with Trisomy 18 were smaller than their matched controls. Fetuses with syndromes varied in the evaluation of their ear surface from those of healthy fetuses. The ear surface can be analyzed with 3D ultrasound and might be useful as a screening parameter in syndrome diagnosis in the future.

Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resolving many problems related to the classification of fetal organs. Processing fetal ultrasound (US) images increasingly uses deep learning (DL) techniques. This paper aims to assess the development of existing DL classification systems for use in a real maternal-fetal healthcare setting. This experimental process has employed two publicly available datasets, such as FPSU23 Dataset and Fetal Imaging. Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. Following the training process, deep features were extracted from the average pooling layers of both models. In a subsequent step, the features from both models were optimized using an improved version of the Generalized Normal Distribution Optimizer (GNDO). Finally, neural networks are used to classify the fused optimized features of both models, which were first combined using a new fusion technique. The best classification scores, 98.5 and 88.6% accuracy, were obtained after multiple steps of analysis. Additionally, a comparison with existing state-of-the-art methods revealed a notable improvement in the suggested architecture's accuracy.

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

For prenatal screening, ultrasound (US) imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as artificial intelligence (AI)-based US image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from US video feeds. AI modelling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.

Fetal face shape analysis from prenatal 3D ultrasound images

3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.