Flywheel Delivers FAIR Principles

The FAIR acronym is a nice way to summarize four important aspirations of modern research practice: scholarly data should be Findable, Accessible, Interoperable, and Reusable. The article describing the FAIR aspirations is excellent, and we recommend reading it. Some limitations of current practice are described here. Our company was founded to advance research and we embrace these principles.

Flywheel, software used by thousands of researchers, embodies tools and technology that deliver on the FAIR principles.

About Flywheel

Flywheel is an integrated suite of software tools that (a) stores data and metadata in a searchable database, (b) includes computational tools to analyze the data, and (c) provides users with both browser-based and command line tools to manage data and perform analyses. Our customers use these tools on a range of hardware platforms: cloud systems, on-premise clusters and servers, and laptops.

Flywheel supports users throughout a project’s life cycle. The software can import data directly from the instrument (like an MR scanner) and extract metadata from the instrument files that is stored into the database. Auxiliary data from other sources can also be imported into the database. The user can view, annotate, and analyze the data, keeping track of all the scientific activities. Finally the data and analyses can be shared widely when it is time to publish the results.

FAIR Data Principals Implemented

Findable

Flywheel makes data ‘Findable’ by search and browsing. The Flywheel search tools address the entire site’s dataset, looking for data with particular features. It is straightforward, for example, to find the diffusion-weighted imaging data for female subjects between the ages of 30 and 45. The user can contact the owners of the data for access, and the data returned by a search can be placed in a virtual project (Collection) for reuse and further analysis.

Search is most effective when there are high quality metadata associated with the data and analyses. Flywheel creates a deep set of metadata by scanning the image data, classifying them. Users can attach specific searchable key words and add data-specific notes at many places - from the overall project level, the session level, the specific data file or the analyses. Users can find data by searching based on these descriptions.

Accessible

Our customers frequently observe that there is a conflict between making data accessible (sharing) while complying with health privacy rules. We live in a world with privacy officers on the one hand and open data advocates on the other.

Flywheel delivers an accessible solution that is respectful of both principles. We implemented a rigorous user-rights management system that is easy to use. Access to the data and analyses is controlled through a simple web-based interface. The system implements the different roles that are needed during a project’s life cycle. At first perhaps only the principal investigator and close collaborators have access; later, additional people (reviewers, other scientists) might be granted access to check the data and analyses. When ready, the anonymized data and full descriptions of the analyses can be made publicly viewable. An effective system that manages a project through these stages is complicated to write, but Flywheel makes the system easy-to-use through its browser interface.

Interoperable

Most scientists have felt the frustration of learning that a dataset is available, but the file format or organization of the data files requires substantial effort to decode and use. The medical imaging community has worked to reduce this burden by defining standardized file and directory organizations. Flywheel is committed to using and promoting these standards.

Our experience teaches us that well intentioned file formats and directory organizations are not enough. Flywheel stores far more information than what one finds in the header of a DICOM or NIfTI file or the BIDS directory structure. Our commitment to interoperability includes reading in files and directories in these standards and even writing Flywheel data into these formats. Beyond this, we are committed to tools that import and export data and metadata between Flywheel and other database systems.

Flywheel is further committed to supporting the interoperability of computational tools. We have opened our infrastructure so that users can analyze data using Flywheel-defined containerized algorithms, their own containers, or their own custom software. The Flywheel standards are clearly defined based on industry-standard formats (e.g., JSON, Docker, Singularity) so that other groups can use them and in this way support computational interoperability.

Reusable

From its inception, Flywheel was designed to make data reusable. Users at a center can share data within their group or across groups, they can reuse the data by combining from different groups, and create and share different computational tools. The user can select data from any project and merge it into a new project. Such reused data is called a Collection in Flywheel. The original data remain securely in place, and the user can analyze the collection as a new virtual project. All the analyses, notes, and metadata of the original data remain attached to the data as they are reused.

Equally important, the computational methods are carefully managed and reusable. Each container for algorithms is accompanied by a precise definition of its control parameters and how they were set at execution time. This combination of container and parameters is called a Flywheel Gear, and the specific Gear that was executed can be reused and shared.

More

The FAIR principles are an important part of the Flywheel system. We have also been able to design in additional functionality that supports these principles.

  • Security and data backup are very important and fundamental. The ability to import older data into the modern technology has been valuable to many of our customers.
  • The visualization tools built into Flywheel help our customers check for accuracy and data quality as soon as the data are part of the system.
  • The programming interface, supported by endpoints accessible in three different scientific programming languages, permits users to test their ideas in a way that gracefully leads to shared data and code.

Tekne 2019 Award Cloud Computing Finalist — Flywheel's Biomedical Imaging Research Platform

MINNEAPOLIS, Sept. 24, 2019: Flywheel.io, a leading biomedical imaging research platform provider and an essential building block for imaging artificial intelligence (AI) has been acknowledged as a finalist for the Minnesota High Tech Association 2019 Tekne Awards in the Cloud Computing category. For the past two decades, the Tekne Awards have recognized organizations that are leading-edge innovators in science and technology in Minnesota.

Flywheel has been named a finalist in the Tekne 2019 Cloud Computing award category for its Biomedical Imaging Research Platform. The Flywheel platform is a comprehensive scientific workflow for imaging data management, including data capture, curation, computation, and collaboration, all integrated to accelerate imaging research within clinical processes, collaborative multi-site studies, and scalable imaging AI initiatives. 

Flywheel is designed to speed and improve imaging research and scientific discovery; from standardizing and structuring massive volumes of historical imaging data in life sciences for AI and Machine Learning, to reducing time and costs for clinical imaging device scanning (magnetic resonance (MR), positron emission tomography (PET), etc...) and study processes, to testing the functionality of research design, all while ensuring research projects adhere to scientific reproducibility, regulatory compliance, security, and collaboration mandates needed for National Institute of Health (NIH) funding requirements.  

Flywheel’s ease-of-use and scalability make Flywheel.io one of the bioinformatic industry's most respected imaging research workflow platforms with global customers including Fortune 500 life science organizations, AI specialists, and dozens of top-tier NIH-funded imaging research centers at universities like Stanford and Columbia.

"We are honored to be named as a finalist for the Cloud Computing Tekne 2019 Awards. Flywheel is privileged to help principal investigators, data scientists, and imaging center directors build imaging research processes for the future...so they can do more science and less IT in their pursuit of healthcare discoveries," said Flywheel CEO, Travis Richarson.

“This year’s Tekne Award finalists demonstrate thought leadership and are spearheading technology innovation in Minnesota and around the world,” said Jeff Tollefson, President and CEO of the Minnesota High Tech Association. “We look forward to further recognizing these organizations at the 2019 Tekne Awards as well as highlighting the impressive science and technology community here in Minnesota.” 

 

A full list of Tekne finalists and November 20 gala details are available online at tekneawards.org.  The event emcee is Paul Douglas.

To learn more about the Flywheel - Biomedical Imaging Research Platform, go to https://flywheel.io

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@Flywheel_io is honored to be a finalist for the Minnesota High Tech Association @MHTA 2019 Tekne Awards in the Cloud Computing category for its biomedical imaging research platform. Go to https://ctt.ac/peqbs+ to learn more about Flywheel. @Flywheel_IO #MachineLearning #ImagingResearch #Bioinformatics

About Flywheel - Biomedical Imaging Research Platform

The Flywheel - Biomedical Imaging Research Platform is the leading provider of scalable and collaborative imaging research processes in an easy-to-use and scientifically sound platform. Flywheel users are creating the building blocks of AI and machine learning based Precision Medicine in a demanding scientific and regulatory environment with high security, privacy, compliance, reproducibility, scalability, and performance requirements. Founded in 2012, Flywheel customers are using millions of imaging research data in Flywheel platform to transform the future of healthcare and medicine.

About the Minnesota High Tech Association (MHTA)

The Minnesota High Tech Association is an innovation and technology association united in fueling Minnesota's prosperity. We bring together the people of Minnesota's science and technology ecosystem and lead the way in bringing science and technology issues to leaders at Minnesota's Capitol and Washington, D.C. MHTA is the only membership organization that represents Minnesota's entire technology-based economy. Our members include organizations of every size − involved in virtually every aspect of technology creation, production, application and education in Minnesota.

Flywheel.io: Brad Canham | 612-223-7359 | bradcanham@flywheel.io

Minnesota High Tech Association: Claire Ayling | 952-230-4553 | cayling@mhta.org


Flywheel Exhibiting and Presenting at RSNA 2019: Bioinformatics Platform for AI Research

On December 1-6, 2019, Flywheel is speaking at the Annual Meeting of the Radiological Society of North America (RSNA) at McCormick Place in Chicago, Illinois. CEO Travis Richardson is presenting on Flywheel’s “Comprehensive Bioinformatics Platform for AI Research” as part of the RSNA 2019 program at 11:30 am on Thursday, Dec. 5th in the AI Theater. Throughout the week, Flywheel is welcoming visitors at Booth 11618 in the AI Showcase. To arrange for a private meeting onsite connect with us here!

 

[Image: CEO, Travis Richardson presenting]

Outdated imaging work processes block AI implementation

In the presentation, Richardson describes the challenges at research hospitals and life sciences organizations to prepare large volumes of imaging data for daily application and artificial intelligence (AI) research. He provides an overview of gaps in PACS, VNAs, and file server systems. Specifically, Richardson details current inabilities to efficiently access data, curate it, search by metadata, and scale large activities to the cloud, like algorithm training and computation.

Richardson reveals how Flywheel addresses these problems in a multi-modal data infrastructure platform that ingests, standardizes, validates, and searches data. Compatible data types include DICOM, FHIR, HL7, biomarker data, and tabular data like genomics through Flywheel’s partnerships with Google Cloud Healthcare API, Google Cloud AutoML, and Google Big Query.

The presentation is of interest to radiologists, imaging lab directors, principal investigators (PIs), and life science teams seeking to avoid IT bottlenecks and improve the speed of imaging AI and scientific discovery.

At RSNA 2019, Flywheel lays the groundwork for biomedical data sharing and machine learning

Flywheel provides a collaborative workflow for imaging research compliant with the requirements of the Food and Drug Administration (FDA), Institutional Review Boards (IRBs), the Health Information Privacy and Portability Act (HIPPA) and General Data Privacy Regulation (GDPR). Inside the Flywheel platform, users create specific imaging research workflows and projects from the curated data. Flywheel also automates reproducibility as required for funding by the National Institute of Health (NIH).

What is RSNA? RSNA 2019 Annual Meeting is the largest radiology conference in the world, showcasing new technologies such as applications for AI. It brings together radiologists, healthcare professionals, hospital administrators, biomedical clinical engineers, IT managers, physicians, and scientists from 115 countries. Flywheel’s presence, among other RSNA 2019 Chicago exhibitors, will facilitate conversations involving multiple disciplines.

Flywheel team members are looking forward to hearing about projects and data governance initiatives and to share their insights into solving biomedical and clinical data challenges.