Fetal Ultrasound Images Research Articles

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.

ANKS6 Variants Underlie Polycystic Kidneys in Prenatal and Neonatal Cases

Background: Nephronophthisis (NPHP) is an autosomal recessive genetic disorder that can cause early-onset kidney failure. ANKS6 plays an important role in early kidney development and encodes a protein that interacts with other proteins within the primary cilium. ANKS6 mutations are known to cause nephronophthisis 16 (NPHP-16). Little is known regarding fetal ultrasound imaging and the antenatal diagnosis of fetuses with ANKS6-associated kidney disease. Here, we report the detection of ANKS6 variants in consanguineous families with polycystic kidney antenatally and in the early stages of life. Methods: Three unrelated Saudi Arabian patients (two prenatal patients and one neonate) were investigated. These cases were referred to the hospital due to the presence of echogenic kidneys on antenatal scanning. After clinical and phenotypic evaluation, whole-exome sequencing (WES) was performed on the cord and peripheral blood to identify the molecular genetic causes associated with the echogenic kidney phenotypes. Results: Two homozygous sequence variants were detected in ANKS6. The homozygous missense novel variant ANKS6: c.1159A>C was detected in Families 1 and 2. In the third family, the known homozygous loss-of-function variant ANKS6: c.907+2T>A was detected. Conclusions: We identified homozygous ANKS6 variants in three families presenting with antenatal polycystic kidney disease. The findings provide an expanded clinical presentation of ANKS6 and emphasize the utility of WES in the diagnosis of echogenic kidneys in prenatal settings.

Deep learning based detection and classification of fetal lip in ultrasound images.

Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips. This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model. The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mean average precision (mAP) at 0.5 and mAP at 0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925. The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips.

Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters

The process of creating fetal images from the uterus using sound influence is known as fetal ultrasound imaging. During this scan, measurements such as the gestational sac, biparietal diameter, head circumference, abdominal circumference, and femur length can be taken from the mother, which are further analyzed by the radiologist or gynecologist. These factors allow us to quickly test for anomalies and monitor the fetal growth and development of a baby. This paper delves into the techniques utilized in previous studies for analyzing abnormalities from ultrasound images using machine learning and deep learning techniques. Specifically, we focus on two trimesters and three key fetal parameters: Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Our proposed method, the Unet segmentation method, not only performs segmentation but also predicts the parameters. We employ various transfer learning techniques for classification. The experiment involves 1,313 medical fetal images, comprising 563 from the second trimester and 750 from the third trimester. In terms of segmentation accuracy, the results for the second trimester's biometric parameters are as follows: AC = 69.09%, FL = 92.02%, and HC = 69.43%. For the third trimester, the accuracy for FL is 90.04%, and for HC, it is 69.76%. Regarding classification methods, MobileNet and XceptionNet yield comparable results. For the second trimester, MobileNet achieves 99.28%, and XceptionNet achieves 99.82%. For the third trimester, both MobileNet and XceptionNet achieve 99.86%.

Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

aims: This study aims to leverage artificial intelligence for enhancing medical diagnosis, focusing on ultrasound evaluation of fetal development and detection of fetal diseases. background: Traditional diagnostic methods in ultrasound are known for being time-consuming and laborious, prompting the need for more efficient approaches. objective: The objective of this research is to develop an end-to-end automatic diagnosis system using convolutional neural networks with ensemble learning to enhance robustness and accuracy in classifying ultrasound images. method: The study involves constructing and implementing the automatic diagnosis system, training it on a diverse dataset encompassing six categories: abdomen, brain, femur, thorax, maternal cervix, and other planes. result: Experimental results demonstrate that the proposed end-to-end system significantly improves the detection accuracy of the standard plane in ultrasound images. conclusion: The application of artificial intelligence through an ensemble learning-based automatic diagnosis system shows promise in advancing ultrasound-based medical diagnosis, particularly in fetal development assessment. other: This research contributes to the ongoing efforts in leveraging technology for more efficient and accurate medical diagnostic processes.

HCN: Hybrid Capsule Network for Fetal Plane Classification in Ultrasound Images

ABSTRACTClassifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture‐based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network‐based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings.

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions. In this study, we propose an automated method for segmenting the pulmonary artery, ascending aorta, and superior vena cava in the 3VV using a novel deep learning network named CoFi-Net. Our network incorporates a coarse-fine collaborative strategy with two parallel branches dedicated to simultaneous global localization and fine segmentation of the vessels. The coarse branch employs a partial decoder to leverage high-level semantic features, enabling global localization of objects and suppression of irrelevant structures. The fine branch utilizes attention-parameterized skip connections to improve feature representations and improve boundary information. The outputs of the two branches are fused to generate accurate vessel segmentations. Extensive experiments conducted on a collected dataset demonstrate the superiority of CoFi-Net compared to state-of-the-art segmentation models for 3VV segmentation, indicating its great potential for enhancing CHD diagnostic efficiency in clinical practice. Furthermore, CoFi-Net outperforms other deep learning models in breast lesion segmentation on a public breast ultrasound dataset, despite not being specifically designed for this task, demonstrating its potential and robustness for various segmentation tasks.

Automatic segmentation based on optimization U-Net neural network (OU-NetNN) for fetal cardiac ultrasound images

Automatic segmentation based on optimization U-Net neural network (OU-NetNN) for fetal cardiac ultrasound images

Ultrasound image denoising autoencoder model based on lightweight attention mechanism.

The presence of noise in medical ultrasound images significantly degrades image quality and affects the accuracy of disease diagnosis. The convolutional neural network-denoising autoencoder (CNN-DAE) model extracts feature information by stacking regularly sized kernels. This results in the loss of texture detail, the over-smoothing of the image, and a lack of generalizability for speckle noise. A lightweight attention denoise-convolutional neural network (LAD-CNN) is proposed in the present study. Two different lightweight attention blocks (i.e., the lightweight channel attention (LCA) block and the lightweight large-kernel attention (LLA) block are concatenated into the downsampling stage and the upsampling stage, respectively. A skip connection is included before the upsampling layer to alleviate the problem of gradient vanishing during backpropagation. The effectiveness of our model was evaluated using both subjective visual effects and objective evaluation metrics. With the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values at all noise levels, the proposed model outperformed the other models. In the test of brachial plexus ultrasound images, the average PSNR of our model was 0.15 higher at low noise levels and 0.33 higher at high noise levels than the suboptimal model. In the test of fetal ultrasound images, the average PSNR of our model was 0.23 higher at low noise levels and 0.20 higher at high noise levels than the suboptimal model. The statistical analysis showed that the p values were less than 0.05, which indicated a statistically significant difference between our model and the other models. The results of this study suggest that the proposed LAD-CNN model is more efficient in denoising and preserving image details than both conventional denoising algorithms and existing deep-learning algorithms.

Deep Learning-Based Architecture for Down Syndrome Assessment During Early Pregnancy Using Fetal Ultrasound Images

Down's syndrome, also known as trisomy 21, is a prevalent genetic condition characterized by intellectual disability and developmental delay in children. The current study has prioritized the development of precise screening techniques for trisomy 21 in the initial stages of pregnancy in order to facilitate prompt diagnosis. This study presents an innovative paradigm for categorizing sagittal views in obstetric ultrasound examinations, specifically identifying Nuchal Translucency, a crucial component within the fetal brain, during the 11th to 14th weeks of pregnancy. The suggested deep learning-based system effectively detects the presence of the essential cerebral structure known as Nuchal Translucency, hence aiding in the diagnosis of Down's syndrome. A dataset comprising more than 1100 pre-processed 2D sagittal-view ultrasound images was gathered to train, test, and validate the proposed convolutional neural network model. The model results were utilized to quantify neurotensin levels and assess the presence of Down's syndrome by image classification. The performance of the model was assessed by measuring its sensitivity, specificity, and area under the curve metrics. These metrics were then compared to those of human experts who had received training in prenatal and ultrasound techniques. Notably, the suggested model attained an outstanding area under the curve score of 0.97. Our study suggests the most common non-invasive method for screening pregnant women for fetal abnormalities is ultrasound, which examines the unborn organs. The application of deep learning and machine learning techniques has significantly enhanced the diagnosis. In order to detect or anticipate conditions like Down syndrome, it assesses the intercranial structures of the developing embryo during the early stages of pregnancy. Developing an improved iteration of this model could serve as the foundation for an effective automated system for diagnosing Down's syndrome in its early stages within a clinical environment.

On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging

Background:To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification. Methods:We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies. We apply both quantitative and qualitative analyses, using standard metrics (F1-score, sensitivity, and precision), Class Activation Mapping (CAM), and t-Distributed Stochastic Neighbor Embedding (t-SNE). Results:When dealing with high inter-class variability classification tasks, contrastive learning does not bring a significant advantage; whereas it results to be relevant for low inter-class variability classification, specifically when initialized with ImageNet weights. Conclusions:Contrastive learning approaches are typically used when a large number of unlabeled data is available, which is not representative of US datasets. We proved that SimClr either as pre-training with backbone initialized via ImageNet weights or used in an end-to-end dual-task may impact positively the performance over standard transfer learning approaches, under a scenario in which the dataset is small and characterized by low inter-class variability.

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

BackgroundCongenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis.MethodIn this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels—the pulmonary artery, aorta, and superior vena cava—in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images.ResultsWe evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively.ConclusionsOur experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models

The ability to detect and track fetal growth is greatly aided by medical image analysis, which plays a crucial role in parental care. This study introduces an attention-guided convolutional neural network (AG-CNN) for maternal–fetal ultrasound image analysis, comparing its performance with that of established models (DenseNet 169, ResNet50, and VGG16). AG-CNN, featuring attention mechanisms, demonstrates superior results with a training accuracy of 0.95 and a testing accuracy of 0.94. Comparative analysis reveals AG-CNN’s outperformance against alternative models, with testing accuracies for DenseNet 169 at 0.90, ResNet50 at 0.88, and VGG16 at 0.86. These findings underscore the effectiveness of AG-CNN in fetal image analysis, emphasising the role of attention mechanisms in enhancing model performance. The study’s results contribute to advancing the field of obstetric ultrasound imaging by introducing a novel model with improved accuracy, demonstrating its potential for enhancing diagnostic capabilities in maternal–fetal healthcare.

Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism

Fetal cardiac ultrasound is a valuable tool for screening fetal cardiac health during pregnancy. Ultrasound standard section testing is an essential part of fetal heart ultrasound diagnosis. Due to the many scattered and partially similar anatomical structures in standard ultrasound views of the fetal heart, the results strongly depend on the clinical experience and knowledge of the sonographer. To improve detection efficiency and reduce misdiagnosis and omission, we propose a single-stage fetal cardiac ultrasound standard plane detection model (FCUM) based on multitask learning and a hybrid attention mechanism to assist sonographers in diagnosis. The feature fusion pyramids of the backbone and detection networks of this model are each embedded with a hybrid attention mechanism module of our design, which enables the multitasking network to extract shared features more accurately and efficiently and improves the accuracy of the detection and classification networks. We designed a classification module for multilayer residual network feature fusion that leads to better classification and faster convergence time. We conducted comprehensive experiments on a dataset of fetal cardiac ultrasound images acquired with several types of devices and in different geographic regions. The experimental results show that our model outperforms baseline models such as YOLOv8 and ResNet-50 in terms of detection precision and classification accuracy.

Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology.

Obstetrics and gynecology (OB/GYN) are areas of medicine that specialize in the care of women during pregnancy and childbirth and in the diagnosis of diseases of the female reproductive system. Ultrasound scanning has become ubiquitous in these branches of medicine, as breast or fetal ultrasound images can lead the sonographer and guide him through his diagnosis. However, ultrasound scan images require a lot of resources to annotate and are often unavailable for training purposes because of confidentiality reasons, which explains why deep learning methods are still not as commonly used to solve OB/GYN tasks as in other computer vision tasks. In order to tackle this lack of data for training deep neural networks in this context, we propose Prior-Guided Attribution (PGA), a novel method that takes advantage of prior spatial information during training by guiding part of its attribution towards these salient areas. Furthermore, we introduce a novel prior allocation strategy method to take into account several spatial priors at the same time while providing the model enough degrees of liberty to learn relevant features by itself. The proposed method only uses the additional information during training, without needing it during inference. After validating the different elements of the method as well as its genericity on a facial analysis problem, we demonstrate that the proposed PGA method constantly outperforms existing baselines on two ultrasound imaging OB/GYN tasks: breast cancer detection and scan plane detection with segmentation prior maps.

Autonomous scanning motion generation adapted to individual differences in abdominal shape for robotic fetal ultrasound

The shortage of obstetricians and gynecologists is increasing in developed countries; therefore, there is a need to improve prenatal care procedures. To automate fetal ultrasound (US) imaging, this paper presents a deep neural network (DNN) model that generates US scan motions for a robot. Additionally, the robot can adapt to the abdominal surface even if the abdominal shape is not precisely known. The latent space of the DNN model is designed to represent the abdominal-shape information. The DNN model can predict the proper trajectory by mapping the height and width of the abdomen to the latent space. Moreover, the robot can detect any deviations from the correct trajectory and return to the right position. For validation, we performed a US scan using the proposed model on tissue-mimicked phantoms that were not used for training. Subsequently, we evaluated the number of dots that were wiped off by the robot from the phantom surface. Overall, the number of dots removed accounted for 91.7% of the total dots. The results demonstrated the feasibility of using the DNN model for motion generation. Hence, the proposed system has the potential to automate fetal US scans according to the individual differences in the abdominal shape.

Explainable AI in Deep Learning Based Classification of Fetal Ultrasound Image Planes

Fetal ultrasound images are widely used for visualizing fetal development during pregnancy. These ultrasound image planes provide information about the anatomy of the fetus, thus helping healthcare professionals identify any abnormalities. Several AI tools are now being applied to classify fetal planes automatically. Accurate classification of fetal ultrasound image planes is crucial for the correct prenatal diagnosis and healthcare. However, while deep learning models have shown promise in image classifications, their ”black box” nature makes their decisions challenging to interpret, which is a significant concern in healthcare analytics. This paper addresses the problem of interpretability of the decisions made by a Convolutional Neural Network(CNN) for fetal ultrasound image classification using an XAI technique. Despite their accuracy, the established solutions lack the transparency essential for medical professionals to trust the model's predictions. The LIME(Local Interpretable Model-agnostic Explanations) is applied to interpret the CNN classification that provides a high classification accuracy. The results of LIME interpretability model on the top of CNN highlight the critical regions that positively and negatively contribute to the classification decision. This approach offers a transparent and trusted solution for leveraging AI in prenatal diagnostics.

A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and requires extensive anatomical knowledge and clinical experience. As such, efficiently and accurately detecting structures from real-world fetal ultrasound images is a key challenge. In this paper, we propose a YOLOX-based deep instance segmentation neural network (i.e., IS-YOLOX) for cardiac anatomical structure location and segmentation in fetal ultrasound images. Specifically, we reconstruct a new instance segmentation branch based on a multi-task deep learning framework. We then design a new multi-level non-maximum suppression (NMS) mechanism to further improve the segmentation performance that consists of three levels of selection. Moreover, unlike two-stage instance segmentation approaches, our method does not rely on object detection results. To the best of our knowledge, this is the first study regarding instance segmentation on 13 types of anatomical structures in the fetal four-chamber view. Extensive experiments were carried out on clinical datasets, and the experimental results show that our method outperforms nine competitive baselines.

SKGC: A General Semantic-Level Knowledge Guided Classification Framework for Fetal Congenital Heart Disease.

Congenital heart disease (CHD) is the most common congenital disability affecting healthy development and growth, even resulting in pregnancy termination or fetal death. Recently, deep learning techniques have made remarkable progress to assist in diagnosing CHD. One very popular method is directly classifying fetal ultrasound images, recognized as abnormal and normal, which tends to focus more on global features and neglects semantic knowledge of anatomical structures. The other approach is segmentation-based diagnosis, which requires a large number of pixel-level annotation masks for training. However, the detailed pixel-level segmentation annotation is costly or even unavailable. Based on the above analysis, we propose SKGC, a universal framework to identify normal or abnormal four-chamber heart (4CH) images, guided by a few annotation masks, while improving accuracy remarkably. SKGC consists of a semantic-level knowledge extraction module (SKEM), a multi-knowledge fusion module (MFM), and a classification module (CM). SKEM is responsible for obtaining high-level semantic knowledge, serving as an abstract representation of the anatomical structures that obstetricians focus on. MFM is a lightweight but efficient module that fuses semantic-level knowledge with the original specific knowledge in ultrasound images. CM classifies the fused knowledge and can be replaced by any advanced classifier. Moreover, we design a new loss function that enhances the constraint between the foreground and background predictions, improving the quality of the semantic-level knowledge. Experimental results on the collected real-world NA-4CH and the publicly FEST datasets show that SKGC achieves impressive performance with the best accuracy of 99.68% and 95.40%, respectively. Notably, the accuracy improves from 74.68% to 88.14% using only 10 labeled masks.

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. A secondary aim was to compare initial screening diagnosis, which made use of live imaging at the point-of-care, with diagnosis by clinicians evaluating only stored images. All pregnancies with isolated severe CHD in the Northwestern region of The Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region and time period. We compared the accuracy of the initial clinical diagnosis (made in real time with access to live imaging) with that of the model (which had only stored imaging available) and with the performance of three blinded human experts who had access only to the stored images (like the model). We analyzed performance according to ultrasound study characteristics, such as duration and quality (scored independently by investigators), number of stored images and availability of screening views. A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity, 53%). Model sensitivity and specificity were 91% and 78%, respectively. Blinded human experts (n = 3) achieved mean ± SD sensitivity and specificity of 55 ± 10% (range, 47-67%) and 71 ± 13% (range, 57-83%), respectively. There was a statistically significant difference in model correctness according to expert-graded image quality (P = 0.03). The abnormal cases included 19 lesions that the model had not encountered during its training; the model's performance in these cases (16/19 correct) was not statistically significantly different from that for previously encountered lesions (P = 0.41). A previously trained DL algorithm had higher sensitivity than initial clinical assessment in detecting CHD in a cohort in which over 50% of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions to which it had not been exposed previously. Furthermore, when both the model and blinded human experts had access to only stored images and not the full range of images available to a clinician during a live scan, the model outperformed the human experts. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.