Application and Progress of Artificial Intelligence in Fetal Ultrasound

Xiao, Sushan; Zhang, Junmin; Zhu, Ye; Zhang, Zisang; Cao, Haiyan; Xie, Mingxing; Zhang, Li

doi:10.3390/jcm12093298

Cited by 18 publications

(10 citation statements)

References 76 publications

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“…ML algorithms are deployed to analyze a broad spectrum of data, encompassing FHR patterns, maternal vital signs, and other clinical parameters. 52 Through these analyses, predictive models are developed, offering insights into the likelihood of fetal distress, preterm birth, or other complications. AI excels in identifying hidden patterns and correlations within extensive datasets, making it particularly adept at identifying early warning signs of potential issues.…”

Section: Ai For Fetal Monitoringmentioning

confidence: 99%

“…AI models heavily rely on robust and diverse datasets for effective training and in some healthcare settings data quality may be suboptimal potentially leading to the development of biased or less accurate models. 52 Moreover, ethical and privacy concerns arise due to the sensitive nature of medical data during pregnancy necessitating a delicate balance between reaping the benefits of AI and safeguarding patient confidentiality and autonomy. 72 The interoperability and integration of AI systems into existing healthcare infrastructure present another layer of complexity.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

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A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in fetomaternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

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Section: Ai For Fetal Monitoringmentioning

confidence: 99%

Section: Challenges and Limitationsmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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“…Congenital heart diseases (CHDs) are classified as common and severe congenital malformations in fetuses, occurring in approximately 6 to 13 out of every 1000 cases [88]. Although, CHDs may have no prenatal symptoms, they may result in significant morbidities, and even death, later in life.…”

Section: Congenital Heart Diseasesmentioning

confidence: 99%

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

Yousefpour Shahrivar,

Karami,

Karami

2023

Biomimetics

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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.

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“…Neural networks, one way to implement artificial intelligence and designed to focus on the algorithms to learn, perform and complete various missions and processes in a human way, have been widely developed in the medical field so far ( 7 , 8 ). “Landmarks” are defined as some points or curves with specific characteristics and corresponding relationships in location and topology in the sense of medical anatomy ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks in prenatal diagnosis of atrioventricular septal defect

Zhou,

Yang,

Ruan

et al. 2024

Transl Pediatr

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Background There is no relevant study on landmarks detection, one of the Convolutional Neural Network algorithms, in the field of fetal echocardiography (FE). This study aimed to explore whether automatic landmarks detection could be used in FE correctly and whether the atrial length (AL) to ventricular length (VL) ratio (AVLR) could be used to diagnose atrioventricular septal defect (AVSD) prenatally. Methods This was an observational study. Two hundred and seventy-eight four-chamber views in end diastole, divided into the normal, AVSD, and differential diagnosis groups, were retrospectively included in this study. Seven landmarks were labeled sequentially by the experts on these images, and all images were divided into the training and test sets for normal, AVSD, and differential diagnosis groups. U-net, MA-net, and Link-net were used as landmark prediction neural networks. The accuracy of the landmark detection, AL, and VL measurements, as well as the prenatal diagnostic effectiveness of AVLR for AVSD, was compared with the expert labeled. Results U-net, MA-net, and Link-net could detect the landmarks precisely (within the localization error of 0.09 and 0.13 on X and Y axis) and measure AL and VL accurately (the measured pixel distance error of AL and VL were 0.12 and 0.01 separately). AVLR in AVSD was greater than in other groups (P<0.0001), but the statistical difference was not obvious in the complete, partial, and transitional subgroups (P>0.05). The diagnostic effectiveness of AVLR calculated by three models, area under receiver operating characteristic curve could reach 0.992 (0.968–1.000), was consistent with the expert labeled. Conclusions U-net, Link-net, and MA-net could detect landmarks and make the measurements accurately. AVLR calculated by three neural networks could be used to make the prenatal diagnosis of AVSD.

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Application and Progress of Artificial Intelligence in Fetal Ultrasound

Cited by 18 publications

References 76 publications

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

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

Application of neural networks in prenatal diagnosis of atrioventricular septal defect

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