Brightheart Sas

Safeheal Sas

Safeheal

Cécile Dupont

Charam Khosrovaninejad

Christophe Gardella

Anne Osdoit

Eric Askinazi

Valentin Thorey

Bertrand Stos

Marilyne Levy

Olivier Tranzer

The present invention relates to a complex surgical device for carrying out an anastomosis in the intestine including:

Systems and methods for introducing a device for protecting an anastomosis

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.

Systems and methods for improving detection of fetal congenital heart defects

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.

Systems and methods for detecting cardiovascular anomalies using spatiotemporal neural networks

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.

Complex surgical device for carrying out and protecting an anastomosis

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.

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.

A surgical device for carrying out an anastomosis in the intestine, including a protective device having an anchor element consisting of a stent and a flexible outer sheath, and an introducer including a first deformable guide tube, where the downstream end of the outer sheath is connected to the first guide tube or to a first connecting piece, the outer sheath being folded inside the first deformable guide tube, and the stent being held in radial compression inside the first deformable guide tube.

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Paris, France

Cécile Dupont