FUJIFILM SonoSite, Inc. Taps the AI2 Incubator to Interpret Ultrasound Images with AI

AI2 Incubator Delivers Deep Learning and Computer Vision Technology on Limited Hardware Capacity to Enable New Levels of Ultrasound Detection

FUJIFILM SonoSite, Inc., specialists in developing cutting-edge, point-of-care ultrasound solutions, and the Allen Institute of Artificial Intelligence (AI2) Incubator, builder of AI-first startups, announced a collaboration to interpret ultrasound images with AI, enabling new ultrasound applications and enhanced accuracy. Fujifilm SonoSite has enlisted assistance from the AI2 Incubator to deploy deep learning models on portable ultrasound products. Together, the AI2 Incubator and Fujifilm SonoSite will work to improve image analysis, allowing for the interpretation of a much wider range of ultrasound scenarios.

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“The AI2 Incubator was a perfect place to look for help in creating breakthrough technology. They have the type of talent that is hard to recruit, combined with the ambition of a startup. We look forward to collaborating more,” said Rich Fabian, President and Chief Operating Officer of FUJIFILM SonoSite, Inc. “The combination of deep learning and medical imaging is very exciting for the future of detection – better care and catching anomalies earlier and faster is a core mission,” confirmed Diku Mandavia, MD, FACEP, FRCPC, Senior Vice President and Chief Medical Officer of FUJIFILM SonoSite, Inc.

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Within the field of medical imaging, deep learning-based techniques have brought breakthroughs across a wide range of scenarios including detecting Tuberculosis (TB) in X-ray scans and diagnosing metastatic breast cancer in pathology slides. Compared to other modalities such as X-ray, CT and PET, ultrasound is more affordable, portable, and does not expose patients to ionizing radiation. Ultrasound’s comparative disadvantage was traditionally its lower image quality. While great improvements have been made over the past two decades, deep learning algorithms now stand to significantly increase both the accuracy and rapid assessment ability of ultrasound technology.

“In tackling this challenge, we are pushing deep learning, computer vision, and medical imaging into uncharted territory,” said Dr. Vu Ha, Technical Director at the AI2 Incubator. “In building new AI-based capabilities in affordable ultrasound devices, we hope to bring them to underserved markets to improve healthcare around the world.”

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