Application and Progress of Artificial Intelligence in Fetal Ultrasound

Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects

Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studies highlight the effectiveness of AI techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), achieving detection accuracy rates of up to 95% across various datasets, including fetal ultrasound images, genetic data, and maternal health records. SVM models have demonstrated 71.50% accuracy on training datasets and 68.57% on testing datasets for NTD classification, while advanced deep learning (DL) methods report patient-level prediction accuracy of 94.5% and an area under the receiver operating characteristic curve (AUROC) of 99.3%. AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. Current AI-assisted ultrasound technologies have improved diagnostic accuracy, yielding sensitivity and specificity rates of 88.9% and 98.0%, respectively, compared to traditional methods with 81.5% sensitivity and 92.2% specificity. AI systems have also streamlined workflows, reducing median scan times from 19.7 min to 11.4 min, allowing sonographers to prioritize critical patient care. Advancements in DL algorithms, including Oct-U-Net and PAICS, have achieved recall and precision rates of 0.93 and 0.96, respectively, in identifying fetal abnormalities. Moreover, AI's evolving role in genetic research supports personalized NTD prevention strategies and enhances public awareness through AI-generated health messages. In conclusion, the integration of AI in prenatal diagnostics significantly improves the detection and assessment of NTDs, leading to greater accuracy and efficiency in ultrasound imaging. As AI continues to advance, it has the potential to further enhance personalized healthcare strategies and raise public awareness about NTDs, ultimately contributing to better maternal and fetal outcomes.

FHD deep learning prognosis approach: Early detection of fetal heart disease (FHD) using ultrasonography image-based IROI combined multiresolution DCNN

Fetal Heart Disease (FHD) is the most prevalent root cause of infant demise which accounts for 21% of all congenital abnormalities, with most instances being catastrophic, thereby rendering the need for early prognosis. Ultrasonography is the forefront imaging modality for assessing fetal growth in four-chamber and blood vessel malformation. Clinically diagnosing the abnormality is time-consuming and requires the skill of a radiologist. In subsequent, numerous preceding research strategies ideal to meta-heuristic and deep learning's Faster Artificial Neural Network (FANN), Dense Recurrent Neural Network (DRNN), Mask-Regional Convolution Neural Network (M RCNN) and Enhanced Deep Learning-assisted CNN aid in the identification of FHD. However, the prediction models have encountered multiple challenges owing to imprecise hinders and irrelevant adhesion. Hence, we propose the automated hierarchical network-driven findings of FHD in four-chamber and blood vessels using ultrasonic 2D imaging which undergoes 3 consequential processes of Enhanced-Adaptive Median Filtering (EAMF) pre-process concerning noise variations i.e., test for SNR distortion and image enhancement i.e., visual quality, Intensified Region of Interest (IROI) segmentation for exploiting feature selection via spatial mask-labeling and Multiresolution Deep Convolutional Neural Network (MDCNN) classification in the detection of diseased pattern via confusion metrics (CM). The lesion findings of CM is determined using MATLAB R2023b with an overall substantial efficiency of 99.79% in both normal and abnormal conditions with a significant potential to assist cardiologists in the prognosis of FHD.

Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review

This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FGR by leveraging advanced machine-learning algorithms and extensive data analysis. Automated fetal biometry using AI has demonstrated significant precision in identifying fetal structures, while predictive models analyzing Doppler indices and maternal characteristics improve the reliability of adverse outcome predictions. AI has enabled early detection and stratification of FGR risk, facilitating targeted monitoring strategies and individualized delivery plans, potentially improving neonatal outcomes. For instance, studies have shown enhancements in detecting placental insufficiency-related abnormalities when AI tools are integrated with traditional ultrasound techniques. This review also explores challenges such as algorithm bias, ethical considerations, and data standardization, underscoring the importance of global accessibility and regulatory frameworks to ensure equitable implementation. The potential of AI to revolutionize prenatal care highlights the urgent need for further clinical validation and interdisciplinary collaboration.

The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles

Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medical disciplines, including obstetrics. The integration of artificial intelligence into 3D/4D ultrasound analysis of fetal facial profiles presents numerous benefits. By leveraging machine learning and deep learning algorithms, AI can assist in the accurate and efficient interpretation of complex 3D/4D ultrasound data, enabling healthcare providers to make more informed decisions and deliver better prenatal care. One such innovation that has significantly improved the analysis of fetal facial profiles is the integration of AI in 3D/4D ultrasound imaging. In conclusion, the integration of artificial intelligence in the analysis of 3D/4D ultrasound data for fetal facial profiles offers numerous benefits, including improved accuracy, consistency, and efficiency in prenatal diagnosis and care.

Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review

The detailed sonographic assessment of the fetal neuroanatomy plays a crucial role in prenatal diagnosis, providing valuable insights into timely, well-coordinated fetal brain development and detecting even subtle anomalies that may impact neurodevelopmental outcomes. With recent advancements in artificial intelligence (AI) in general and medical imaging in particular, there has been growing interest in leveraging AI techniques to enhance the accuracy, efficiency, and clinical utility of fetal neurosonography. The paramount objective of this focusing review is to discuss the latest developments in AI applications in this field, focusing on image analysis, the automation of measurements, prediction models of neurodevelopmental outcomes, visualization techniques, and their integration into clinical routine.

Comprehensive evaluation of fetal renal ultrasound parameters for fetal growth restriction

Objective

This study aims to investigate variances in renal ultrasound parameters between fetuses experiencing fetal growth restriction (FGR) and those with normal intrauterine development, with the intent to offer actionable insights for clinical management.

Method

Forty-five pregnant women diagnosed with FGR between 28 and 36 weeks of gestation, who underwent examination at Wenzhou People's Hospital from September 2021 to June 2023, constituted the FGR group. Concurrently, 65 pregnant women with normal intrauterine development at matching gestational weeks formed the control group. Renal ultrasound parameters, encompassing renal artery peak systolic velocity (PSV), end diastolic velocity (EDV), time averaged maximum velocity (TAMX), resistive indices (S/D, PI, RI), ratios of renal volume to gestational age (RV/WEEK) and estimated fetal weight (RV/EFW), vascular indices (VI, FI, VFI), were compared between the two groups. All parameters represented the mean values of bilateral kidneys.

Result

In the FGR group, fetal renal artery PSV (37.71 ± 9.93 cm/s), EDV (6.19 ± 1.50 cm/s), TAMX (15.10 ± 3.83 cm/s), RV/WEEK (0.45 ± 0.12), RV/EFW (7.53 ± 3.24), VI (22.19 ± 15.00), and VFI (5.53 ± 3.63) were significantly lower compared to the control group (PSV: 47.11 ± 11.24 cm/s, EDV: 7.13 ± 2.00 cm/s, TAMX: 17.85 ± 3.85 cm/s, RV/WEEK: 0.66 ± 0.19, RV/EFW:9.20 ± 3.17, VI: 28.67 ± 14.72, VFI: 7.40 ± 3.68). Conversely, fetal renal artery resistive indices (S/D: 9.09 ± 2.58, PI: 2.71 ± 0.56, RI: 0.92 ± 0.04) in the FGR group were notably higher than those in the control group (S/D: 6.22 ± 1.93, PI: 2.20 ± 0.73, RI: 0.87 ± 0.04), with statistical significance (P < 0.05). No significant difference was found in renal FI between the FGR group (26.78 ± 6.59) and the control group (26.89 ± 5.82) (P > 0.05). Receiver operating characteristic (ROC) curve analysis revealed higher diagnostic efficacy for RV/WEEK and RI among individual indicators, while combined parameter application yielded the highest diagnostic efficiency.

Conclusion

Utilizing a comprehensive evaluation of fetal kidney ultrasound parameters with multiple indices facilitates early screening and diagnosis of FGR fetuses, thereby aiding clinical decision-making and enhancing newborn birth outcomes.

Antenatal screening for fetal structural anomalies - Routine or targeted practice?

Antenatal screening with ultrasound identifies fetal structural anomalies in 3-6% of pregnancies. Identification of anomalies during pregnancy provides an opportunity for counselling, targeted imaging, genetic testing, fetal intervention and delivery planning. Ultrasound is the primary modality for imaging the fetus in pregnancy, but magnetic resonance imaging (MRI) is evolving as an adjunctive tool providing additional structural and functional information. Screening should start from the first trimester when more than 50% of severe defects can be detected. The mid-trimester ultrasound balances the benefits of increased fetal growth and development to improve detection rates, whilst still providing timely management options. A routine third trimester ultrasound may detect acquired anomalies or those missed earlier in pregnancy but may not be available in all settings. Targeted imaging by fetal medicine experts improves detection in high-risk pregnancies or when an anomaly has been detected, allowing accurate phenotyping, access to advanced genetic testing and expert counselling.

Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility

Maternal health remains a critical global health challenge, with disparities in access to care and quality of services contributing to high maternal mortality and morbidity rates. Artificial intelligence (AI) has emerged as a promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, and expanding access to care. This review explores the transformative role of AI in maternal healthcare, focusing on its applications in the early detection of pregnancy complications, personalized care, and remote monitoring through AI-driven technologies. AI tools such as predictive analytics and machine learning can help identify at-risk pregnancies and guide timely interventions, reducing preventable maternal and neonatal complications. Additionally, AI-enabled telemedicine and virtual assistants are bridging healthcare gaps, particularly in underserved and rural areas, improving accessibility for women who might otherwise face barriers to quality maternal care. Despite the potential benefits, challenges such as data privacy, algorithmic bias, and the need for human oversight must be carefully addressed. The review also discusses future research directions, including expanding AI applications in maternal health globally and the need for ethical frameworks to guide its integration. AI holds the potential to revolutionize maternal healthcare by enhancing both care quality and accessibility, offering a pathway to safer, more equitable maternal outcomes.

First-trimester ultrasound of the cerebral lateral ventricles in fetuses with open spina bifida: a retrospective cohort study

BACKGROUND

Beyond 18 weeks of gestation, an increased size of the fetal lateral ventricles is reported in most fetuses with open spina bifida. In the first trimester of pregnancy, the definition of ventriculomegaly is based on the ratio of the size of the choroid plexus to the size of the ventricular space or the entire fetal head. However, contrary to what is observed from the midtrimester of pregnancy, in most fetuses with open spina bifida at 11 to 13 weeks of gestation, the amount of fluid in the ventricular system seems to be reduced rather than increased.

OBJECTIVE

This study aimed to compare the biometry of the lateral ventricles at 11 0/7 to 13 6/7 weeks of gestation between normal fetuses and those with confirmed open spina bifida.

STUDY DESIGN

This was a retrospective cohort study that included all cases of isolated open spina bifida detected at 11 0/7 to 13 6/7 weeks of gestation over a period of 5 years and a group of structurally normal fetuses attending at our center over a period of 1 year for the aneuploidy screening as controls. Transventricular axial views of the fetal brain obtained from cases and controls were extracted from the archive for post hoc measurement of cerebral ventricles. The choroid plexus-to-lateral ventricle length ratio, sum of the choroid plexus-to-lateral ventricle area ratio, choroid plexus area-to-fetal head area ratio, and mean choroid plexus length-to-occipitofrontal diameter ratio were calculated for both groups. The measurements obtained from the 2 groups were compared, and the association between each parameter and open spina bifida was investigated.

RESULTS

A total of 10 fetuses with open spina bifida were compared with 358 controls. Compared with controls, fetuses with open spina bifida showed a significantly smaller size of the cerebral ventricle measurements, as expressed by larger values of choroid plexus-to-lateral ventricle area ratio (0.49 vs 0.72, respectively; P<.001), choroid plexus-to-lateral ventricle length ratio (0.70 vs 0.79, respectively; P<.001), choroid plexus area-to-fetal head area ratio (0.28 vs 0.33, respectively; P=.006), and choroid plexus length-to-occipitofrontal diameter ratio (0.52 vs 0.60, respectively; P<.001). The choroid plexus-to-lateral ventricle area ratio was found to be the most accurate predictor of open spina bifida, with an area under the curve of 0.88, a sensitivity of 90%, and a specificity of 82%.

CONCLUSION

At 11 0/7 to 13 6/7 weeks of gestation, open spina bifida is consistently associated with a reduced amount of fluid in the lateral cerebral ventricles of the fetus, as expressed by a significantly increased choroid plexus-to-lateral ventricle length ratio, choroid plexus-to-lateral ventricle area ratio, choroid plexus area-to-fetal head area ratio, and choroid plexus length-to-occipitofrontal diameter ratio.

How automated techniques ease functional assessment of the fetal heart: Applicability of two-dimensional speckle-tracking echocardiography for comprehensive analysis of global and segmental cardiac deformation using fetalHQ®

BACKGROUND

Prenatal echocardiographic assessment of fetal cardiac function has become increasingly important. Fetal two-dimensional speckle-tracking echocardiography (2D-STE) allows the determination of global and segmental functional cardiac parameters. Prenatal diagnostics is relying increasingly on artificial intelligence, whose algorithms transform the way clinicians use ultrasound in their daily workflow. The purpose of this study was to demonstrate the feasibility of whether less experienced operators can handle and might benefit from an automated tool of 2D-STE in the clinical routine.

METHODS

A total of 136 unselected, normal, singleton, second- and third-trimester fetuses with normofrequent heart rates were examined by targeted ultrasound. 2D-STE was performed separately by beginner and expert semiautomatically using a GE Voluson E10 (FetalHQ®, GE Healthcare, Chicago, IL). Several fetal cardiac parameters were calculated (end-diastolic diameter [ED], sphericity index [SI], global longitudinal strain [EndoGLS], fractional shortening [FS]) and assigned to gestational age (GA). Bland-Altman plots were used to test agreement between both operators.

RESULTS

The mean maternal age was 33 years, and the mean maternal body mass index prior to pregnancy was 24.78 kg/m2. The GA ranged from 16.4 to 32.0 weeks (average 22.9 weeks). Averaged endoGLS value of the beginner was -18.57% ± 6.59 percentage points (pp) for the right and -19.58% ± 5.63 pp for the left ventricle, that of the expert -14.33% ± 4.88 pp and -16.37% ± 5.42 pp. With increasing GA, right ventricular endoGLS decreased slightly while the left ventricular was almost constant. The statistical analysis for endoGLS showed a Bland-Altman-Bias of -4.24 pp ± 8.06 pp for the right and -3.21 pp ± 7.11 pp for the left ventricle. The Bland-Altman-Bias of the ED in both ventricles in all analyzed segments ranged from -.49 mm ± 1.54 mm to -.10 mm ± 1.28 mm, that for FS from -.33 pp ± 11.82 pp to 3.91 pp ± 15.56 pp and that for SI from -.38 ± .68 to -.15 ± .45.

CONCLUSIONS

Between both operators, our data indicated that 2D-STE analysis showed excellent agreement for cardiac morphometry parameters (ED and SI), and good agreement for cardiac function parameters (EndoGLS and FS). Due to its complexity, the application of fetal 2D-STE remains the domain of scientific-academic perinatal ultrasound and should be placed preferably in the hands of skilled operators. At present, from our perspective, an implementation into clinical practice "on-the-fly" cannot be recommended.

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

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 feto-maternal 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.

Application of neural networks in prenatal diagnosis of atrioventricular septal defect

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.

Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain

BACKGROUND

The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks.

OBJECTIVES

We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics

BACKGROUND

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care.

OBJECTIVE

The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process.

METHODS

A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion.

RESULTS

According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload.

CONCLUSION

The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

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.