This year included live meeting sessions running from 8 a.m. to 6 p.m. on November 29 to December 5th. If you were unable to attend the live programming as it happened, it converted to on demand sessions thereafter. One advantage of the live programming sessions was the ability to chat with other attendees while it was occurring. However, not all of the live programming sessions were in fact live even though they broadcasted originally live. Instead many of these live sessions occurred without the normal moderator present and were prerecorded. The meeting also included on-demand content available 24/7 during that time period. This year many exhibitors had virtual booths for exhibition and there was also virtual networking available. For those who paid for a premium registration, there was also the benefit of extended on-demand access to most meeting content until April 30, 2021. In addition, case of the day and digital posters were available at RSNA 2020. Navigation presented to users upon login to RSNA 2020 was as below:

Outside of virtual booths, this year included various ways to get the latest on industry developments including two theaters: an Innovation Theater and AI (artificial intelligence) Theater. This was in addition to Lunch & Learns, Featured Demonstrations, Pre Show Presentations, and Roundtable Discussions. There was also an Imaging AI In Practice Demonstration including four videos on the following topics: 1) Introduction, 2) Work with AI, 3) Evolve with AI, and 4) Save Time with AI. These Imaging AI In Practice Demonstration videos included helpful flow-charts to describe how the AI Orchestrator and AI Algorithms integrate into the radiology workflow:

There was certainly a large amount of content focused around AI at RSNA 2020. I attended a session titled Artificial Intelligence: Beyond Interpretive Considerations that consisted of four separate talks. One talk in this session discussed how there was minimal merger and acquisition activity in radiology AI in 2020 and there were six companies involved with radiology AI that previously attended RSNA in prior years that appear to no longer be active. Another talk discussed Generative Adversarial Networks and their potential to create synthetic data in radiology. An example was shown on using a StyleGan2 (a type of GAN) to create synthetic chest radiographs. Another talk discussed the liability risks in using AI in medicine. Those potentially at risk include radiologists, healthcare systems, and even AI developers. The presenter discussed how due to lack of meaningful case law to date a lot remains unknown. I also attended another session titled Creating Publicly Accessible Radiology Imaging Resources for Machine Learning and AI. One talk in this session discussed using the The Cancer Imaging Archive (TCIA) as a dataset for AI training. Another talk mentioned some other datasets including the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Medical Information Mart for Intensive Care (MIMIC), National Biomedical Imaging Archive (NBIA), the Lung Image Database Consortium (LIDC), and datasets from Stanford University’s Center for Artificial Intelligence in Medicine and Imaging. In addition, discussion was made of efforts from developing the Medical Imaging and Data Resource Center (MIDRC), an open-source database with medical images from COVID-19 patients, being collaborated on across more than 20 organizations in the U.S.

This year there appeared to be less focus on 3D printing. Although one live session titled Medical 3D Printing Regulatory and Quality Considerations was available. One talk in this session discussed sterilization for 3D printed devices. There was discussion made of sterilization approaches for 3D printed devices both in the hospital (steam, hydrogen peroxide, and ethylene oxide) and outside the hospital (gamma radiation, e-beam, hydrogen peroxide, and ethylene oxide). There were also at least two on-demand sessions available in the Innovation Theater from two industry leaders in 3D printing: 1) Extending Access to Extended Reality from Materialise and 2) Learnings from the Field: Clinical Care and Device Development in the COVID Era from Formlabs Medical.

The sponsors of RSNA 2020 included those below:

If you are interested learning more about past in person RSNA meetings at McCormick Place, I attended the RSNA annual meeting last year in 2019, and the prior two years in 2018 and 2017. You can find more information and numerous photographs at http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2019-at-mccormick-place, http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2018-at-mccormick-place/, and http://www.toddmccollough.com/radiological-society-north-america-rsna-chicago-il-2017-mccormick-place/.

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This year also expanded focus on 3D Printing and Advanced Visualization with an expanded Showcase and Theater offering daily presentations on the latest research and innovations in 3D printing for medical applications. I was able to attend a presentation covering Category III CPT Codes for 3D Printing of Anatomic Models and Guides, Scripting for Segmentation, 3D Printing to Support Research, and Leveraging 3D Printing for Surgical Simulation. It was quite interesting to hear more about the Category III CPT Codes for 3D Printing, which includes 0559T, 0560T, 0561T, and 0562T that went into effect in July, 2019. This should allow for greater adoption by physicians and medical centers. Even so, for those utilizing 3D printing, it was encouraged by the presenter of the CPT code talk to sign up for the RSNA-ACR 3D Printing Registry to help support a future category I CPT code.

As usual there were numerous posters and presentations. Also as usual, there were many exhibitors with medical imaging devices ready to provide demonstrations of their latest technology. New exhibitors this year included Amazon Web Services (AWS) and Medical IP. I was able to attend a few educational courses and scientific sessions. In particular I attended the Artificial Intelligence: Cutting Edge Artificial Intelligence session and Creating Publicly Accessible Radiology Imaging Resources for Machine Learning and AI sessions. In the former session mentioned above, an interesting talk titled Defacing Neuroimages discussed image de-identification using a two-step deep learning model for head CTs and brain MRIs. In the former session, I also was intrigued by a talk titled Automated Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks that discussed automatically detecting vertebral fractures in CT images of the spine using a learning method with 3D features. The latter session featured several talks discussing practical challenges with data preparation including image pre-processing steps, techniques for creating ground truth labeling, and statistical approaches to create training and testing data sets.

Below are some of the pictures I took while at the RSNA annual meeting in 2019, in Chicago, IL.

I attended the RSNA annual meeting last year in 2018 and the prior year in 2017, where you can find more information and photos at http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2018-at-mccormick-place/ and http://www.toddmccollough.com/radiological-society-north-america-rsna-chicago-il-2017-mccormick-place/.

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