Brightheart Sas

Bertrand Stos

Cécile Dupont

Marilyne Levy

Christophe Gardella

Valentin Thorey

Eric Askinazi

Olivier Tranzer

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 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.

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.

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Le Plessis Robinson, France

Bertrand Stos