Feature: Radiology-Grade Viewer with Image Annotation
Flywheel has made significant upgrades to our native DICOM viewer to meet the needs of radiologists performing safety reads or participating in blind multi-reader studies. The integrated viewer can now load full studies with a simple click of the button at the project and session level. The new work list interface and radiology-grade tools make loading, reading, and annotating cases fast and easy.
Our ROI and annotation tools allow users to efficiently annotate imaging data sets for applications such as machine learning. ROI labels may be customized to meet project-specific needs and ensure that annotations are consistent and standardized. Qualitative summary forms may be created on any project to capture the results of radiology reads in a structured manner, thereby eliminating the need to use an external spreadsheet or similar approach. All ROIs and qualitative summaries are searchable and easily accessible via Data Views and the Flywheel SDKs for subsequent analysis.
Flywheel is also releasing a new WADO (Web Access to DICOM Object) service that allows you to access imaging data through your viewer of choice or integrate with existing DICOM workflows.
Four AI Workflow Trends from RSNA 2019
The Biggest Trend: Maturing Implementation of AI
Attendees who visited our booth last year were interested in learning about AI capabilities. This year they were bringing questions about implementing infrastructure needed for AI and how to scale AI research in their organizations. Scaling access to clinical data and interoperability appears to be a rising concern this year. Organizations are also gradually accepting cloud scaling as a secure option.
Radiologists are beginning to plan for AI in their standard workflows. There were many radiologists in our booth asking questions with respect to AI research in their current clinical workflows.
Data Curation for Research Still Falls Short
The focus in many workshops and presentations from radiologists was “data wrangling” and data set quality. We received many questions from attendees regarding metadata management and labelling tools. At the same time there is growing recognition that clinical systems don’t meet the needs of the research and AI development communities. Additionally, an entirely new class of solution that supports the research workflow is needed.
We recommend Dr. Paul Chang’s (University of Chicago) AuntMinnie interview during RSNA: “AI is like a great car … Most cars still need gas and roads. In the context of this analogy, gas is vetted data and the road is workflow orchestration that is AI-enabled... The only way to make a transformative technology real is to do the boring stuff, the infrastructure stuff.”
Everyone Noticed the Busy AI Showcase
The AI Showcase was very active this year. In 2018, there were roughly 70 vendors in the AI Showcase, but this year there were 129, including many international AI vendors. We noticed growth in AI development for cardiac and brain imaging.
It’s Imminent: Equipment Vendors are Integrating AI Workflows
AI is moving beyond the desktop as imaging equipment manufacturers have their eye on supporting research workflows. Leading equipment manufacturers like Philips and Canon displayed developments in their interfaces to support AI or analysis tools in a disease specific applications. Flywheel is expanding partnerships with AI vendors and equipment vendors in addition to supporting clients performing imaging and clinical research.
Four Takeaways from BioData World West 2019
BioData World West wrapped up its third year! A mix of experts from industry, academia, and government mingled and mused on the data management supporting the healthcare industry.
Below are the insights from our own Chief Technology Officer, Gunnar Schaefer and Director of Sales, Marco Comianos, who attended.
Gunnar Schaefer, Co-Founder and CTO of @Flywheel_io presents on scaling medical imaging and machine learning in clinical research
Share quality data within your organization
The main focus among conversations at BioData this year was making data accessible across departments and organizations. Letting data flow freely between labs in life sciences organizations creates a feedback loop from health network partners and previously unprofitable drug trials. In health networks, data scientists can highlight opportunities where patients are underserved to create better experiences and processes that can be streamlined to cut costs.
When these different sources of data are merged, unconventional combinations of biomedical data can point to obscure patterns of disease. Scientists from organizations like GenomeAsia, Sidra Medicine, and AstraZeneca presented their findings from blending microbiome and genetic research, genotypic and phenotypic data, and imaging and text data.
In order for machine learning to power artificial intelligence applications, data must be routed, organized, cleaned, and standardized from the moment of creation. More important than proper data storage is the ability to query a system over and over for renewed insight. Genentech introduced the need to store data so it is FAIR: findable, accessible, interoperable, and reusable. That way, data are ripe for query and can integrate together for analysis.
However, it’s important to remember that no matter how well sources are linked together, data must be high-quality and machine learning investigations must be ethically supervised. As Faisal Khan of AstraZeneca put it: “Tortured data will confess to anything.”
Looking forward, expect life sciences companies to adopt better data principles in their data strategies, refine what’s working already, and search for software that bridges the gaps.
Being precise about requirements for precision medicine
Much of the groundwork for precision medicine is now being laid, though mostly in oncology. At BioData, speakers gave direction for its high-value applications.
Today’s genomics research can treat previously-untreated rare diseases. A panel addressed how data sharing must accompany public genomic projects to optimize therapeutic development for rare diseases. Presenters also reported on diversifying the pools for large genome projects. On the treatment side, analysts explained methods to match an individual’s genomic profile with one out of many pre-existing drugs, saving time for patients facing debilitating diseases.
These advancements require access to large amounts of data with well-defined interoperability. Looking forward, expect the general hype around precision medicine to fade, making way for discussions about infrastructure which enable answers to disease-specific precision questions.
Machine learning shortens both ends of drug trials
Beyond the potential for drug discovery using genetic markers, algorithms were showcased which had correctly predicted the pharmacokinetics and effectiveness of drug compounds. Not only does this technology assist researchers and cut costs for developing compounds or finding targets, once therapies are in clinical trials, AI can predict the likelihood of certain subpopulations having an adverse reaction to a drug. Clinical trial pools normally miss these portions of the population, which can result in a public perception crisis.
Looking forward, expect to see AI use with historical clinical data and patient data becoming a competitive factor in shortening the time horizon for successful drug launches. We’ll also see which AI vendors become the most productive partners for life sciences organizations.
AI specialists come ready to partner
If data scientists hold some healthy skepticism of practically applying machine learning, AI specialists showed up with the energy to compensate. AI specialists are drawing talent from universities to specialize in anatomical regions. Companies in this vertical are also starting to partner with each other to complement their deep expertise in one region.
Many AI companies at BioData specialize in genomics and digital slide pathology, so look forward to development and consolidation in this field. Fewer imaging analysis companies were present at BioData - stay tuned for the imaging market insights yet to come out of RSNA!
At RSNA’s Annual Meeting, Flywheel will be exhibiting from December 1st to December 5th in the AI Showcase. Schedule a demo and find us at booth #11618.
High Performance Computing (HPC) with Flywheel
Flywheel now provides additional support for customers’ existing infrastructure and workflows with two significant advances.
HPC with Singularity and Slurm
Flywheel has introduced support for High Performance Computing environments running singularity and Slurm. This integration enables users to save on computing costs and allows them to take advantage of significant investments made in existing computing resources. Flywheel Gears may be run on HPC clusters regardless of whether the Flywheel system is deployed on premises or in the cloud.
Rsync-like Data Access
CLI Sync provides users with an efficient method of syncing their Flywheel data with local file systems. This improved data portability allows customers to leverage pipelines and hardware not yet integrated with Flywheel. The rsync-like functionality ensures that only changes to data are migrated to local systems, thus saving time and cost.
Flywheel Introduces New Advanced Search
Flywheel augments its fast, powerful search tool with the introduction of Advanced Search, providing users the ability to construct complex queries and quickly pinpoint the data they need. When this feature is introduced, find Advanced Search by hitting ‘Enter’ in the Search Bar and clicking ‘Advanced Search’.
Visual Query Builder
Advanced Search features a visual query builder, providing even the least technical users with the ability to quickly and easily construct highly specific queries. Search queries may be saved for sharing and reuse.
Powerful SQL-Like Query Language
Users that are more familiar with SQL can manually construct queries using Flywheel’s simplified query language: FlyQL. FlyQL enables access to all metadata, including DICOM tags and custom metadata.
Search Image Annotations and ROIs
With more and more customers looking to Flywheel to manage large, diverse data sets for Clinical Trials and Machine Learning projects, the need for improved data management tools has come to the forefront. With advanced search, users will have the ability to quickly and easily locate data across multiple projects matching any number of criteria. AI Developers can search for annotated images, including by regions of interest (ROIs), and create machine learning training sets with ease. Researchers can quickly locate subjects based on custom metadata to create collections or run batch analysis directly from search results.
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!
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.