Edwards Lifesciences research
Clinical-AI research under privacy constraints.
Make ultrasound usable for research without removing the diagnostic evidence the research depended on.
Research across privacy-aware cardiac-ultrasound preparation, weak-supervision experiments, and an independent EchoNet-Dynamic reimplementation.
Cardiac-ultrasound frames can carry identifying material outside the diagnostic cone. A blunt crop could protect identity while erasing color-Doppler evidence the research needed.
Preparation therefore treated de-identification as evidence preservation, not generic image cleaning.
Nixense contributed privacy-aware preparation, experimental implementation, and independent benchmark work in a collaborative research setting. Collaboration is not presented as deployment or sole authorship.
Instance segmentation, semantic segmentation, and classical projection were compared against whether the exterior was masked while Doppler information inside the cone survived.
The experiment progressed through increasingly complex synthetic bags and inspected positive/negative instance balance before acceptance.
The independent EchoNet-Dynamic path followed segmentation, video regression, cardiac-cycle extraction, and beat-level aggregation. It established a reproducible research reference — not clinical validation or superiority over Stanford.
No Edwards product deployment, clinical-care effect, regulatory approval, validated clinical performance, or sole Nixense authorship is claimed.
The work established a privacy-aware data path, weak-supervision experiments, and a reproducible benchmark reference. It did not establish clinical performance or production deployment.