Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis

Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ultrasound (US) and magnetic resonance imaging (MRI), with a particular focus on multimodal integration to leverage their complementary strengths. By critically analyzing state-of-the-art AI methodologies, including deep learning frameworks and attention-based architectures, this study highlights significant advancements alongside persistent challenges. Notable barriers include the scarcity of diverse and high-quality datasets, computational inefficiencies, and ethical concerns surrounding data privacy and security. Special attention is given to multimodal approaches that integrate US and MRI, combining the accessibility and real-time imaging of US with the superior soft tissue contrast of MRI to improve diagnostic precision. Furthermore, this review emphasizes the transformative potential of AI in fostering clinical adoption through innovations such as real-time diagnostic tools and human-AI collaboration frameworks. By providing a comprehensive roadmap for future research and implementation, this study underscores AI's potential to redefine fetal imaging practices, enhance diagnostic accuracy, and ultimately improve perinatal care outcomes.

Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Novel Techniques in Antenatal Imaging of Spinal Dysraphisms

PURPOSE OF REVIEW

This review examines the imaging techniques for diagnosing spinal dysraphisms (SD), focusing on advancements in prenatal detection.

RECENT FINDINGS

Prenatal ultrasound (US) is the first-line tool for detecting spinal dysraphisms, including myelomeningocele. While US is effective for early detection, it has limitations in fully characterizing defects, particularly due to factors like fetal positioning. To address these, advanced techniques such as 3D ultrasound and AI-driven algorithms have improved diagnostic accuracy. Magnetic resonance imaging (MRI) remains critical for a comprehensive evaluation, providing detailed visualization of soft tissue anomalies and assessing lesion severity. Prenatal ultrasound is essential for initial screening but often complemented by MRI for a thorough diagnosis. Innovations in imaging technologies, including AI and 3D ultrasound, promise to enhance early detection and clinical management of spinal dysraphisms.

Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning

Fetal biometric assessments through ultrasound diagnostics are integral in obstetrics and gynecology, requiring considerable time investment. This study aimed to explore the potential of artificial intelligence (AI) architectures in automatically identifying fetal abdominal standard scanning planes and structures, particularly focusing on the abdominal circumference. Ultrasound images from a prospective cohort study were preprocessed using CV2 and Keras-OCR to eliminate textual elements and artifacts. Optical character recognition detected and removed textual components, followed by inpainting using adjacent pixels. Six deep learning neural networks, Xception and MobileNetV3Large, were employed to categorize fetal abdominal view planes. The dataset included nine classes, and the models were evaluated through a tenfold cross-validation cycle. The MobileNet3Large and EfficientV2S achieved accuracy rates of 79.89% and 79.19%, respectively. Data screening confirmed non-normal distribution, but the central limit theorem was applied for statistical analysis. ANOVA test revealed statistically significant differences between the models, while Tukey's post hoc tests showed no difference between MobileNet3Large and EfficientV2S, while outperforming the other networks. AI, specifically MobileNet3Large and EfficientV2S, demonstrated promise in identifying fetal abdominal view planes, showcasing potential benefits for prenatal ultrasound diagnostics. Further studies should compare these AI models with established methods for automatic abdominal circumference measurement to assess overall performance.

Zoom Pattern Signatures for Fetal Ultrasound Structures

During a fetal ultrasound scan, a sonographer will zoom in and zoom out as they attempt to get clearer images of the anatomical structures of interest. This paper explores how to use this zoom information which is an under-utilised piece of information that is extractable from fetal ultrasound images. We explore associating zooming patterns to specific structures. The presence of such patterns would indicate that each individual anatomical structure has a unique signature associated with it, thereby allowing for classification of fetal ultrasound clips without directly reading the actual fetal ultrasound images in a convolutional neural network.

Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial

OBJECTIVE

Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees during their scanning procedures. The aim of this study was to investigate the potential benefits of utilizing such an artificial intelligence system to enhance the efficiency of obstetric ultrasound training in acquiring and interpreting standard basic views.

METHODS

A prospective, single-center randomized controlled study was conducted at The First Affiliated Hospital of Sun Yat-sen University. From September 2022 to April 2023, residents with no prior obstetric ultrasound experience were recruited and assigned randomly to either a PSAIS-assisted training group or a conventional training group. Each trainee underwent a four-cycle practical scan training program, performing 20 scans in each cycle on pregnant volunteers at 18-32 gestational weeks, focusing on acquiring and interpreting standard basic views. At the end of each cycle, a test scan evaluated trainees' ability to obtain standard ultrasound views without PSAIS assistance, and image quality was rated by both the trainees themselves and an expert (in a blinded manner). The primary outcome was the number of training cycles required for each trainee to meet a certain standard of proficiency (i.e. end-of-cycle test scored by the expert at ≥ 80%). Secondary outcomes included the expert ratings of the image quality in each trainee's end-of-cycle test and the discordance between ratings by trainees and the expert.

RESULTS

In total, 32 residents and 1809 pregnant women (2720 scans) were recruited for the study. The PSAIS-assisted trainee group required significantly fewer training cycles compared with the non-PSAIS-assisted group to meet quality requirements (P = 0.037). Based on the expert ratings of image quality, the PSAIS-assisted training group exhibited superior ability in acquiring standard imaging views compared with the conventional training group in the third (P = 0.012) and fourth (P < 0.001) cycles. In both groups, the discordance between trainees' ratings of the quality of their own images and the expert's ratings decreased with increasing training time. A statistically significant difference in overall trainee-expert rating discordance between the two groups emerged at the end of the first training cycle and remained at every cycle thereafter (P < 0.013).

CONCLUSION

By assisting inexperienced trainees in obtaining and interpreting standard basic obstetric scanning views, the use of artificial intelligence-assisted systems has the potential to improve training effectiveness. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Probe motion during mid-trimester fetal anomaly scan in the clinical setting: A prospective observational study

INTRODUCTION

This study aims to investigate probe motion during full mid-trimester anomaly scans.

METHODS

We undertook a prospective, observational study of obstetric sonographers at a UK University Teaching Hospital. We collected prospectively full-length video recordings of routine second-trimester anomaly scans synchronized with probe trajectory tracking data during the scan. Videos were reviewed and trajectories analyzed using duration, path metrics (path length, velocity, acceleration, jerk, and volume) and angular metrics (spectral arc, angular area, angular velocity, angular acceleration, and angular jerk). These trajectories were then compared according to the participant level of expertise, fetal presentation, and patient BMI.

RESULTS

A total of 17 anomaly scans were recorded. The average velocity of the probe was 12.9 ± 3.4 mm/s for the consultants versus 24.6 ± 5.7 mm/s for the fellows (p = 0.02), the average acceleration 170.4 ± 26.3 mm/s2 versus 328.9 ± 62.7 mm/s2 (p = 0.02), and the average jerk 7491.7 ± 1056.1 mm/s3 versus 14944.1 ± 3146.3 mm/s3 (p = 0.02), the working volume 9.106 ± 4.106 mm3 versus 29.106 ± 11.106 mm3 (p = 0.03), respectively. The angular metrics were not significantly different according to the participant level of expertise, the fetal presentation, or to patients BMI.

CONCLUSION

Some differences in the probe path metrics (velocity, acceleration, jerk and working volume) were noticed according to operator's level.

The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard-to-identify fetal cardiac anomalies?

Linked article: This Editorial comments on articles by Day et al. and Taksøe‐Vester et al.

Cost-utility analysis of prenatal diagnosis of congenital cardiac diseases using deep learning

BACKGROUND

Deep learning (DL) is a new technology that can assist prenatal ultrasound (US) in the detection of congenital heart disease (CHD) at the prenatal stage. Hence, an economic-epidemiologic evaluation (aka Cost-Utility Analysis) is required to assist policymakers in deciding whether to adopt the new technology.

METHODS

The incremental cost-utility ratios (CUR), of adding DL assisted ultrasound (DL-US) to the current provision of US plus pulse oximetry (POX), was calculated by building a spreadsheet model that integrated demographic, economic epidemiological, health service utilization, screening performance, survival and lifetime quality of life data based on the standard formula: CUR = Increase in Intervention Costs - Decrease in Treatment costs Averted QALY losses of adding DL to US & POX US screening data were based on real-world operational routine reports (as opposed to research studies). The DL screening cost of 145 USD was based on Israeli US costs plus 20.54 USD for reading and recording screens.

RESULTS

The addition of DL assisted US, which is associated with increased sensitivity (95% vs 58.1%), resulted in far fewer undiagnosed infants (16 vs 102 [or 2.9% vs 15.4%] of the 560 and 659 births, respectively). Adoption of DL-US will add 1,204 QALYs. with increased screening costs 22.5 million USD largely offset by decreased treatment costs (20.4 million USD). Therefore, the new DL-US technology is considered "very cost-effective", costing only 1,720 USD per QALY. For most performance combinations (sensitivity > 80%, specificity > 90%), the adoption of DL-US is either cost effective or very cost effective. For specificities greater than 98% (with sensitivities above 94%), DL-US (& POX) is said to "dominate" US (& POX) by providing more QALYs at a lower cost.

CONCLUSION

Our exploratory CUA calculations indicate the feasibility of DL-US as being at least cost-effective.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound

BACKGROUND

Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements. Our study tested the hypothesis that a deep learning method can estimate fetal weight based on a video scan of the fetal abdomen and gestational age with similar performance to the full biometry-based estimations provided by clinical experts.

OBJECTIVE

This study aimed to develop and test a deep learning method to automatically estimate fetal weight from fetal abdominal ultrasound video scans.

STUDY DESIGN

A dataset of 900 routine fetal ultrasound examinations was used. Among those examinations, 800 retrospective ultrasound video scans of the fetal abdomen from 700 pregnant women between 15 6/7 and 41 0/7 weeks of gestation were used to train the deep learning model. After the training phase, the model was evaluated on an external prospectively acquired test set of 100 scans from 100 pregnant women between 16 2/7 and 38 0/7 weeks of gestation. The deep learning model was trained to directly estimate fetal weight from ultrasound video scans of the fetal abdomen. The deep learning estimations were compared with manual measurements on the test set made by 6 human readers with varying levels of expertise. Human readers used standard 3 measurements made on the standard planes of the head, abdomen, and femur and heuristic formula to estimate fetal weight. The Bland-Altman analysis, mean absolute percentage error, and intraclass correlation coefficient were used to evaluate the performance and robustness of the deep learning method and were compared with human readers.

RESULTS

Bland-Altman analysis did not show systematic deviations between readers and deep learning. The mean and standard deviation of the mean absolute percentage error between 6 human readers and the deep learning approach was 3.75%±2.00%. Excluding junior readers (residents), the mean absolute percentage error between 4 experts and the deep learning approach was 2.59%±1.11%. The intraclass correlation coefficients reflected excellent reliability and varied between 0.9761 and 0.9865.

CONCLUSION

This study reports the use of deep learning to estimate fetal weight using only ultrasound video of the fetal abdomen from fetal biometry scans. Our experiments demonstrated similar performance of human measurements and deep learning on prospectively acquired test data. Deep learning is a promising approach to directly estimate fetal weight using ultrasound video scans of the fetal abdomen.

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.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

BACKGROUND

Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening. Hence, the aim of this systematic review is to gain insight into future research directions needed to bring automated early-pregnancy ultrasound analysis of the human brain to clinical practice.

METHODS

We searched PubMed (Medline ALL Ovid), EMBASE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar, from inception until June 2022. This study is registered in PROSPERO at CRD42020189888. Studies about computational methods for the analysis of human brain ultrasonography acquired before the 20th week of pregnancy were included. The key reported attributes were: level of automation, learning-based or not, the usage of clinical routine data depicting normal and abnormal brain development, public sharing of program source code and data, and analysis of the confounding factors.

FINDINGS

Our search identified 2575 studies, of which 55 were included. 76% used an automatic method, 62% a learning-based method, 45% used clinical routine data and in addition, for 13% the data depicted abnormal development. None of the studies shared publicly the program source code and only two studies shared the data. Finally, 35% did not analyse the influence of confounding factors.

INTERPRETATION

Our review showed an interest in automatic, learning-based methods. To bring these methods to clinical practice we recommend that studies: use routine clinical data depicting both normal and abnormal development, make their dataset and program source code publicly available, and be attentive to the influence of confounding factors. Introduction of automated computational methods for early-pregnancy brain ultrasonography will save valuable time during screening, and ultimately lead to better detection, treatment and prevention of neuro-developmental disorders.

FUNDING

The Erasmus MC Medical Research Advisor Committee (grant number: FB 379283).