Research Registry – avoiding the loss of knowledge

Future of Healthcare

Rhino HealthTech, Inc. | November 24, 2021

Rhino Health today announced a pilot project with the pancreatic cancer working group of the National Cancer Institute's Early Detection Research Network (EDRN), focused on cross-institution collaboration to improve outcomes for people who have been diagnosed with or are predisposed to developing pancreatic cancer. Using federated learning, participating institutions hope to recruit more collaborators and expedite the execution of large-scale research without the encumberment caused by the current need to share data. The investigators will utilize multi-modal data — including CT scans, cinematic renderings, and laboratory test results — to create AI models that accelerate diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC). When PDAC is diagnosed earlier, the likelihood of survival is substantially higher. The researchers hope to enable earlier diagnosis and tailoring of more precise treatment. Participating institutions include Johns Hopkins Medicine, MD Anderson Cancer Center, Dana-Farber Cancer Institute, University of Pittsburgh Medical Center, Cedars-Sinai Medical Center, and City of Hope National Medical Center. "One of the biggest challenges in pancreatic research is accessing the large datasets required to come to scientific conclusions," said Elliot Fishman, MD, Professor of Radiology, Surgery, Oncology and Urology at Johns Hopkins Hospital. "No one institution can do it alone. Cross-institutional collaboration is essential to changing the trajectory for pancreatic cancer patients, and federated learning makes it possible for multiple researchers to utilize relevant data while always protecting privacy and without creating additional administrative or IT burden." With federated learning, AI models are trained using data from disparate sources — without sharing or aggregating data. This protects privacy, facilitates access to more diverse datasets, and makes it easier for medical researchers and AI developers around the world to collaborate. Rhino Health is an NVIDIA partner and member of the NVIDIA Inception program. Rhino Health is leveraging NVIDIA's federated learning technology in the Rhino Health Platform — an end-to-end federated learning solution that makes it possible for researchers to quickly get a project up and running and easily add collaborators. "Rhino Health is putting the power of federated learning in the hands of leading medical researchers, building on the industry-leading capabilities made possible by NVIDIA's federated learning technology," said Mona Flores, MD, head of medical AI at NVIDIA. "This platform approach is well-aligned with our vision for the future of federated learning, which we believe will fundamentally change how healthcare AI is developed and deployed." "To realize the transformative promise of healthcare AI in the early detection of pancreatic cancer — and more broadly across the practice of radiology — we need to collectively adopt common standards and principles in managing and utilizing data," said Eugene Koay, MD, PhD, Associate Professor, Department of GI Radiation Oncology at MD Anderson Cancer Center. "Together, medical researchers and industry are doing this, and federated learning helps to ensure we're keeping observations in context, maintaining high-quality data, and collaborating in a very transparent manner that ultimately serves patients." EDRN, backed by the National Cancer Institute (NCI), is a consortium of more than 300 investigators at academic institutions and in the private sector working to discover, develop, and validate biomarkers and imaging methods to detect early-stage cancers. The consortium is also working to assess risk for developing cancer and translate biomarkers and imaging methods into clinical tests. "Translating research findings into clinical practice requires assurance that an AI model will work consistently across today's increasingly diverse real-world patient populations," said Michael Rosenthal, MD, PhD, Assistant Professor of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School. "This means we need to be utilizing diverse datasets from the early stages of research, and federated learning is critical to providing globally relevant generalizable methods of finding pancreatic cancer earlier." Several of the principal investigators in this pilot project will present their work and the intended collaboration with Rhino Health during a virtual industry presentation at the upcoming annual meeting of the Radiological Society of North America (RSNA). The session will take place online on Tuesday, November 30, from 12:15-1:15 p.m. Central Time, titled "Accelerating AI: How Federated Learning Can Protect Privacy, Facilitate Collaboration and Improve Outcomes." "We are humbled by the ingenuity and dedication of these leading physicians and scientists, who are revolutionizing clinical medicine using AI. For that, they need to be able to collaborate effectively and efficiently, using a common platform and without the risk of patient privacy breach. We hope that Rhino Health's 'Federated Learning as a Platform' solution will be a useful tool at their disposal to help accelerate the impact of healthcare AI." Ittai Dayan, MD, co-founder and CEO of Rhino Health About Rhino Health The Rhino Health Platform allows medical researchers and healthcare AI developers to seamlessly access diverse and disparate datasets and use them to create better AI algorithms. Grounded in federated learning, Rhino Health makes it possible to collaborate without ever moving data, transferring ownership, or risking patient privacy. Headquartered in Boston, MA, Rhino Health is a growing team of healthcare, AI and technology experts committed to accelerating creation and adoption of AI-based healthcare solutions for increasingly diverse patient populations.

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Penn Medicine, Intel | May 13, 2020

Perelman School of Medicine is working with Intel to advance a new distributed machine learning technique. The technique allows for researchers at disparate organizations around the world to work together to develop new deep learning initiatives without having to share data. The research will be trained on the largest brain tumor dataset to date, without identifiable patient data leaving the individual collaborators. Perelman School of Medicine at the University of Pennsylvania is working with Intel to advance a new distributed machine learning technique – one that allows for researchers at disparate organizations around the world to work together to develop new deep learning initiatives without having to share data. WHY IT MATTERS That approach, which aims for big advances in collaborative AI research while still maintaining patient privacy, is called federated learning. Researchers at Penn Medicine have published findings that a federated-learning approach to medical-imaging AI could train a model to more than 99% of the accuracy of a model trained in the traditional, non-private method. Read More: CMS announced an increase in inpatient rates, new hospital payment category for CAR T So Penn is working with 29 research institutions from the U.S. – as well as in Canada, the U.K., Germany, the Netherlands, Switzerland and India – using Intel technology to deploy a federated-learning approach to develop new deep learning models for identifying brain tumors. n order to train and build a model to detect a brain tumor that could aid in early detection and better outcomes, these researchers need access to large amounts of relevant medical data. By using federated learning, they will be able to work together on building and training an algorithm to detect a brain tumor while protecting sensitive medical data. Penn Medicine and Intel say the research will be trained on the largest brain tumor dataset to date, without identifiable patient data leaving the individual collaborators. THE LARGER TREND Even if more evidence is needed, AI and machine learning are proving their worth in the field of medical imaging. In April, for instance, a study on AI-augmented diabetic retinopathy screening indicated that such programs are cheaper than human grading – that a deep learning system would save on the roughly two minutes of human labor required to grade each case. The Penn Medicine and Intel initiative is funded by a three-year, $1.2 million grant from the Informatics Technology for Cancer Research program of the National Institutes of Health. Read More: Prescryptive Health and Bartell Drugs Team up to Expand Access to Reliable and Secure COVID-19 Antibody Testing Some of the collaborating institutions planning to participate in the first phase of this federation are the Hospital of the University of Pennsylvania, Washington University in St. Louis, the University of Pittsburgh Medical Center, Vanderbilt University, Queen's University, Technical University of Munich, University of Bern, King's College London and Tata Memorial Hospital. ON THE RECORD It is widely accepted by our scientific community that machine learning training requires ample and diverse data that no single institution can hold, this year, the federation will begin developing algorithms that identify brain tumors from a greatly expanded version of the International Brain Tumor Segmentation challenge dataset. This federation will allow medical researchers access to vastly greater amounts of healthcare data while protecting the security of that data. - Dr. Spyridon Bakas at Penn's Center for Biomedical Image Computing and Analytics. AI shows great promise for the early detection of brain tumors, but it will require more data than any single medical center holds to reach its full potential, Using Intel software and hardware and support from some of Intel Labs’ brightest minds, we are working with the University of Pennsylvania and a federation of 29 collaborating medical centers to advance the identification of brain tumors while protecting sensitive patient data. - Jason Martin, principal engineer at Intel Labs. About Penn Medicine Penn Medicine’s mission is to advance knowledge and improve health through research, patient care, and the education of trainees in an inclusive culture that embraces diversity, fosters innovation, stimulates critical thinking, supports lifelong learning, and sustains our legacy of excellence. Penn Medicine includes six acute-care hospitals and hundreds of outpatient centers throughout the region. Our hospitals include The Hospital of the University of Pennsylvania, Penn Presbyterian Medical Center, Pennsylvania Hospital, Chester County Hospital, Lancaster General Health and Penn Medicine Princeton Health. About Intel Intel is a technology leader focused on unleashing the potential of data so that we can fulfill our purpose: Create world-changing technology that enriches the lives of every person on earth. This is our ultimate motivation and why we exist. We are in the midst of an exciting journey to go beyond just PCs and servers to a company that is at the center of a new era where everything looks and acts like a computer. The explosion of devices and data has led to several technology inflections—AI, 5G network transformation, and the rise of the intelligent edge—that, together

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marketresearchjournalist | July 09, 2019

In this report, Marketinsightsreports studies the present scenario (with the base year being 2018) and the growth prospects of global Blockchain In Healthcare market for 2019-2025. The report titled Global Blockchain In Healthcare Market provides an in-depth analysis of the key development strategies and market trend dynamics which includes drivers, challenges, and opportunities prevailing in the industry. The report provides an extensive insight into various forms of developments, trends and key participants.

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