Abstract: Systems and methods are provided for processing image data generated by a medical imaging device such as an ultrasound or echocardiogram device and processing the image data using artificial intelligence and machine learning to determine a presence of one or more congenital heart defects (CHDs) and/or other cardiovascular anomalies in the image data, to identify anatomy, detect and/or identify motion, and/or to determine key-point and/or contour detection. The image processing system may be used to detect CHDs and/or other cardiovascular anomalies in a fetus. The image data may be processed using a spatiotemporal convolutional neural network (CNN). The spatiotemporal CNN may include a spatial CNN for image recognition and a temporal CNN for processing optical flow data and/or image data. The outputs of the spatial CNN and the temporal CNN may be fused (e.g., using late fusion) and/or may be processed by a spatiotemporal CNN.

Abstract: Systems and methods are provided for aiding the detection and diagnosis of critical heart defects during fetal ultrasound examinations, in which image data (e.g., motion video clips and/or image frames) is analyzed with machine learning algorithms to detect and identify morphological and/or flow abnormalities indicative of critical CHDs. The results of the analyses are presented for review to the clinician, optionally with an overlay, for the selected image frames that identifies the abnormalities with graphical or textual indicia. The overlay further may be annotated by the clinician and stored to create documentary record of the fetal ultrasound examination.

Abstract: Systems and methods are provided for processing image data generated by a medical imaging device such as an ultrasound or echocardiogram device and processing the image data using artificial intelligence and machine learning to determine a presence of one or more congenital heart defects (CHDs) and/or other cardiovascular anomalies in the image data and/or to determine key-point and/or contour detection. The image processing system may be used to detect CHDs and/or other cardiovascular anomalies in a fetus. The image data may be processed using a spatiotemporal convolutional neural network (CNN). The spatiotemporal CNN may include a spatial CNN for image recognition and a temporal CNN for processing optical flow data based on the image data. The outputs of the spatial CNN and the temporal CNN may be fused (e.g., using late fusion) to generate a likelihood of CHDs and/or other cardiovascular anomalies.

Abstract: Systems and methods are provided for aiding the detection and diagnosis of critical heart defects during fetal ultrasound examinations, in which image data (e.g., motion video clips and/or image frames) are analyzed with machine learning algorithms to identify and select image frames within the image data that correspond to standard views recommended by fetal ultrasound guidelines, and selected image frames are analyzed with machine learning algorithms to detecting and identify morphological abnormalities indicative of critical CHDs associated with the standard views. The results of the analyses are presented for review to the clinician with an overlay for the selected image frames that identifies the abnormalities with graphical or textual indicia. The overlay further may be annotated by the clinician and stored to create documentary record of the fetal ultrasound examination.

Abstract: Systems and methods are provided for aiding the detection and diagnosis of critical heart defects during fetal ultrasound examinations, in which image data (e.g., motion video clips and/or image frames) are analyzed with machine learning algorithms to identify and select image frames within the image data that correspond to standard views recommended by fetal ultrasound guidelines, and selected image frames are analyzed with machine learning algorithms to detecting and identify morphological abnormalities indicative of critical CHDs associated with the standard views. The results of the analyses are presented for review to the clinician with an overlay for the selected image frames that identifies the abnormalities with graphical or textual indicia. The overlay further may be annotated by the clinician and stored to create documentary record of the fetal ultrasound examination.

Abstract: Systems and methods are provided for processing image data generated by a medical imaging device such as an ultrasound or echocardiogram device and processing the image data using artificial intelligence and machine learning to determine a presence of one or more congenital heart defects (CHDs) and/or other cardiovascular anomalies in the image data and/or to determine key-point and/or contour detection. The image processing system may be used to detect CHDs and/or other cardiovascular anomalies in a fetus. The image data may be processed using a spatiotemporal convolutional neural network (CNN). The spatiotemporal CNN may include a spatial CNN for image recognition and a temporal CNN for processing optical flow data based on the image data. The outputs of the spatial CNN and the temporal CNN may be fused (e.g., using late fusion) to generate a likelihood of CHDs and/or other cardiovascular anomalies.

Abstract: Systems and methods are provided for processing image data generated by a medical imaging device such as an ultrasound or echocardiogram device and processing the image data using artificial intelligence and machine learning to determine a presence of one or more congenital heart defects (CHDs) and/or other cardiovascular anomalies in the image data. The image processing system may be used to detect CHDs and/or other cardiovascular anomalies in a fetus. The image data may be processed using a spatiotemporal convolutional neural network (CNN). The spatiotemporal CNN may include a spatial CNN for image recognition and a temporal CNN for processing optical flow data based on the image data. The outputs of the spatial CNN and the temporal CNN may be fused (e.g., using late fusion) to generate a likelihood of CHDs and/or other cardiovascular anomalies.

Abstract: Systems and methods are provided for aiding the detection and diagnosis of critical heart defects during fetal ultrasound examinations, in which motion video clips are analyzed with machine learning algorithms to identify and select within the motion video clips image frames that correspond to standard views recommended by fetal ultrasound guidelines, and selected image frames are analyzed with machine learning algorithms to detecting and identify morphological abnormalities indicative of critical CHDs associated with the standard views. The results of the analyses are presented for review to the clinician with an overlay for the selected image frames that identifies the abnormalities with graphical or textual indicia. The overlay further may be annotated by the clinician and stored to create documentary record of the fetal ultrasound examination.

Abstract: Systems and methods are provided for processing image data generated by a medical imaging system such as an ultrasound or echocardiogram system using artificial intelligence and machine learning to determine a presence of one or more congenital heart defects (CHDs) and/or other cardiovascular anomalies in the image data in a manner that is agnostic to the type of imaging system, software, and/or hardware. Image data from various types imaging systems, software, and/or hardware, having various styles of imaging data generated may be processed to determine image styles. Input image data for analysis may then be processed together with representative styles of image data to generate styled input images for each style. The styled input images may be processed by an image analyzer to detect one or more cardiovascular anomalies in the styled image data, for example. Alternatively, training data may be styled and used to train the image analyzer.