Sep 9–12, 2019

AI and Deep Learning enable 4x faster scans and productivity gains for clinical radiology

Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
1:45pm2:25pm Thursday, September 12, 2019
Location: 230 B

Who is this presentation for?

CEO/CIO/CFO in healthcare industry (hospital, imaging centers, pharmaceutical manufacture, healthcare IT)




AI and Deep Learning are driving healthcare innovation and clinical radiology. In this talk, we will introduce FDA cleared AI solutions that enable faster and safer radiology exams, deliver better care to the patients, and provide immediate and quantifiable values to hospitals and imaging centers.

The solutions are designed to address the following issues facing clinical radiology: 1) Imaging exams (such as MRI and PET) are very inefficient and expensive. 2) usage of radiation and contrast dosage result in risks to patients. 3) increasing needs of compacity and the patients’ needs urge radiology practice to improve both quality and productivity.

Subtle Medical provide Deep Learning solution, cleared by FDA and accelerated by industry frameworks, such as Intel OpenVINO, to address these problems by enabling 4x-10x faster MRI scans, 4x faster PET scans and 10x dosage reduction.


1) SubtlePET and SubtleMR: 4x-10x faster exams
For faster PET and MRI exams, Deep Learning solution is proposed to replace the conventional iterative optimization based algorithm. Using ResNet based recurrent structure and Generative Adversarial Network based adversarial loss, the model successfully reconstructs images from low-quality images into high-quality images with significantly better Signal-to-Noise Ratio and resolution information. This solution generates results much more efficient than conventional iterative methods with much better image quality and accuracy.
Based on the deep learning algorithm, we developed SubtlePET and SubtleMR that are the first AI software products FDA cleared for medical imaging enhancement.

2) SubtleGad: 10x contrast dose reduction
Gadolinium Deposition is one of the most urgent issues facing radiology community. In this work, we validated both technical performance and clinical applicability of the developed Deep Learning (DL) technology that algorithmically restores/boosts the contrast information in MRI. We further verified the generalization and robustness of the DL solution and show it improves workflow while maintaining diagnostic quality.

【Discussion and Conclusion】

Deployment with Industry solution such as Intel OpenVINO provides ~10x speed up of the inference by optimizing the network model. This application, with great accuracy, scalability, and efficiency, can significantly improve the workflow for clinical radiology to provide faster, safer and smart imaging exams to patients.

Results from clinical partners and deployment sites such as Hoag Hospital and UCSF demonstrate the immediate values AI can benefit hospitals and imaging centers. Applications also show the value of the applications to clinical trial and pharmaceutical developments.

Prerequisite knowledge

Background and interests in AI and healthcare

What you'll learn

1. AI and Deep Learning are driving innovation in healthcare and clinical radiology. 2. AI can not only be used for assisted diagnosis but also for improving entire imaging workflow and productivity, enabling immediate financial values to hospitals. 3. Solutions developed by Subtle Medical and Stanford University use AI to significantly improve the efficiency and quality of radiology. 4. The products are FDA cleared and well received by clinicians. Example of clinical and financial evaluation at hospitals validate our assumption. 5. Powered by industry solution such as Intel OpenVINO, the deployment can be highly accelerated and enables real-time solutions that benefit clinical applications.
Photo of Enhao Gong

Enhao Gong

Subtle Medical

Enhao Gong is founder and CEO at Subtle Medical. He is a serial entrepreneur and PhD in Electrical Engineering at Stanford, with research focus on applying AI and deep learning to improve reconstruction, analysis and quantification in medical imaging . His work that applies AI to accelerate and reduce dose for MRI and PET has been featured in numbers of academic journals and clinical conferences. Dr. Gong won several awards including 2018 Forbes China/Asia 30-under-30 for his work at Subtle Medical, an AI+radiology startup from Stanford and the winner of 2018 NVIDIA Inception Award in AI+Healthcare.

Photo of Greg Zaharchuk

Greg Zaharchuk

Stanford University

Greg Zaharchuk is a radiologist and professor in radiology at Stanford University and a neuroradiologist at Stanford Hospital. His research interests include deep learning applications in neuroimaging, imaging of cerebral hemodynamics with MRI and CT, noninvasive oxygenation measurement with MRI, clinical imaging of cerebrovascular disease, imaging of cervical artery dissection, MR/PET in neuroradiology, and resting-state fMRI for perfusion imaging and stroke.

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