News | Ultrasound Imaging | September 23, 2019

Fujifilm Sonosite Partnering With Artificial Intelligence Incubator to Improve Ultrasound Image Interpretation

AI2 Incubator delivers deep learning and computer vision technology on limited hardware capacity to enable new levels of ultrasound detection

Fujifilm Sonosite Partnering With Artificial Intelligence Incubator to Improve Ultrasound Image Interpretation

September 23, 2019 — Fujifilm SonoSite Inc. 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.

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, computed tomography (CT) and positron emission tomography (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 Vu Ha, Ph.D., 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.”

For more information: www.sonosite.com, www.incubator.allenai.com

Related Content

Schematic diagram of the proposed multichannel deep neural network model analyzing multiscale functional brain connectome for a classification task. rsfMRI = resting-state functional MRI.

Schematic diagram of the proposed multichannel deep neural network model analyzing multiscale functional brain connectome for a classification task. rsfMRI = resting-state functional MRI. Graphic courtesy of the Radiological Society of North America.

News | Artificial Intelligence | December 11, 2019
December 11, 2019 — Deep learning, a type of arti...
CT_Pediatric_Scan_Philips_Vereos_CT_RSNA 2016

Image courtesy of Philips Healthcare

News | Pediatric Imaging | December 10, 2019
December 10, 2019 — More than half of people who received...
Damage from concussion alters the way information is transmitted between the two halves of the brain, according to a new study presented today at the annual meeting of the Radiological Society of North America (RSNA).

Image courtesy of RSNA

News | Clinical Trials | December 10, 2019
December 10, 2019 — Damage from...
 MRI system cardiac scan from DeBakey Hospital
News | Magnetic Resonance Imaging (MRI) | December 05, 2019
December 5, 2019 — The following is by Lawr