AI and deep learning enable 4x faster scans and productivity gains for clinical radiology
Who is this presentation for?
- CEOs, CIOs, and CFOs in the healthcare industry (hospitals, imaging centers, pharmaceutical manufacturers, and healthcare IT)
AI and deep learning drive healthcare and clinical radiology innovations. Enhao Gong and Greg Zaharchuk detail FDA-cleared AI solutions that enable faster and safer radiology exams, deliver better care to patients, and provide immediate and quantifiable values to hospitals and imaging centers. The solutions are designed to address issues facing clinical radiology such as the inefficiency and expense of imaging exams (such as MRI and PET), the use of radiation and contrast dosage resulting in risks to patients, and the increasing needs of compacity and the patients’ needs urge radiology practice to improve both quality and productivity.
Subtle Medical provides a deep learning solution, cleared by the 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. For faster PET and MRI exams, the 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 into high-quality with significantly better signal-to-noise ratio and resolution information. This generates results much more efficiently than conventional iterative methods with much better image quality and accuracy. Based on the deep learning algorithm, Subtle Medical developed SubtlePET and SubtleMR, the first AI software products FDA cleared for medical imaging enhancement. Gadolinium deposition is one of the most urgent issues facing the radiology community. SubtleGad, the deep learning technology, algorithmically restores or boosts the contrast information in MRI. Subtle Medical further verified the generalization and robustness of the DL solution and have shown it to improve workflow while maintaining diagnostic quality.
Deployment with industry solutions such as Intel OpenVINO provides a ~10x speedup 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 smarter imaging exams to patients. Results from clinical partners and deployment sites such as Hoag Hospital and UCSF demonstrate the immediate benefits AI can provide to hospitals and imaging centers. Applications also show the value of the applications to clinical trial and pharmaceutical developments.
- Familiarity with AI and healthcare
What you'll learn
- See how AI and deep learning drive innovation in healthcare and clinical radiology
- Understand that AI can not only be used for assisted diagnosis but also for improving entire imaging workflow and productivity, enabling immediate financial values to hospitals
- Explore the solutions developed by Subtle Medical and Stanford University that use AI to significantly improve the efficiency and quality of radiology
- Learn how the deployment can be accelerated, which enables real-time solutions that benefit clinical applications
Enhao Gong is the founder and CEO at Subtle Medical, an AI and radiology startup from Stanford and the winner of 2018 NVIDIA Inception Award at AI+Healthcare. He’s a serial entrepreneur and PhD in electrical engineering at Stanford, with a research focus on applying AI and deep learning to improve reconstruction, analysis, and quantification in medical imaging. His work applies AI to accelerate and reduce doses for MRI and PET and has been featured in numbers of academic journals and clinical conferences. Enhao has won several awards, including being recognized by Forbes China as one of 2018’s “30 under 30.”
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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