News | Artificial Intelligence | September 11, 2020

RSNA Launches Pulmonary Embolism AI Challenge

The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism.

Getty Images

September 11, 2020 — The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism.

RSNA collaborated with the Society of Thoracic Radiology (STR) to create a massive dataset for the challenge. The RSNA-STR Pulmonary Embolism CT (RSPECT) dataset is comprised of more than 12,000 CT scans collected from five international research centers. The dataset was labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.

“The dataset created for this year’s challenge is the largest publicly available collection of expert-annotated pulmonary embolism CT data for AI,” said John Mongan, M.D., M.P.H., vice chair of the Machine Learning Steering Subcommittee of the RSNA Radiology Informatics Committee. “We  anticipate that this year’s challenge will ignite interest in pulmonary embolism detection as an AI use case by demonstrating what can be achieved with a very large, well annotated multi-institutional dataset, and will advance radiology toward improving patient care with AI.”

Last year’s intracranial hemorrhage detection and classification challenge attracted more than 1,300 teams to develop algorithms to identify and classify subtypes of hemorrhages on head CT. The dataset, comprised of more than 25,000 head CT scans, was the first multiplanar dataset used in an RSNA AI Challenge.

RSNA organizes these challenges to spur the creation of AI tools that will enhance the efficiency and accuracy of radiologic diagnoses.

To build these tools, AI researchers need access to volumes of imaging data annotated by expert radiologists. Data challenges engage the radiology community to develop such datasets, which provide the standard of truth in training AI systems to perform tasks relevant to diagnostic imaging.

In a challenge, researchers compete on how well their AI models perform defined tasks according to specified performance measures. Each AI challenge explores and demonstrates the ways AI can benefit radiology and improve patient care.

The RSNA-STR Pulmonary Embolism Detection Challenge is being conducted on a platform provided by Kaggle, Inc., and is open to everyone. For the first time this year, RSNA’s challenge will adopt the approach of a code submission competition, intended to produce models that are more efficient and readily usable. Final models must be submitted by October 26. The top 10 performing competitors will be awarded a total of $30,000.

Results will be announced on November 23, and the top submissions will be recognized during the virtual RSNA annual meeting (RSNA 2020, Nov. 29 – Dec. 5). 

For more information on the challenge, visit RSNA.org/AI-image-challenge or Kaggle.com/pulmonary-embolism-detection. The AI data challenges are organized by the RSNA Radiology Informatics Committee: [email protected].

Related Content

The impact of deploying artificial intelligence (AI) for radiation cancer therapy in a real-world clinical setting has been tested by Princess Margaret researchers in a unique study involving physicians and their patients.

Getty Images

News | Artificial Intelligence | June 15, 2021
June 15, 2021 — The impact of deploying ...
A cardiac MRI of athletes who had COVID-19 is seven times more effective in detecting inflammation of the heart than symptom-based testing, according to a study led by researchers at The Ohio State University Wexner Medical Center and College of Medicine with 12 other Big Ten programs.

Cardiac Magnetic Resonance Imaging in Athletes With Clinical and Subclinical Myocarditis A-D, Athlete A with subclinical possible myocarditis was asymptomatic with normal electrocardiogram (ECG), echocardiogram, and high-sensitivity troponin findings. A, T2 mapping showing elevated T2 in basal-mid inferolateral wall in short axis view. B, late gadolinium enhancement (LGE) in the basal inferolateral wall in short axis view. C, Postcontrast steady state-free precession (SSFP) images showing contrast uptake in the basal-mid inferolateral wall in short axis view. D, LGE in the inferolateral wall in 3-chamber view. E-H, Athlete B with subclinical probable myocarditis was asymptomatic with normal ECG, normal echocardiogram, and elevated high-sensitivity troponin findings. E, T2 mapping showing elevated T2 in the anteroseptal wall in short axis view. F, LGE in the anteroseptal wall in 3-chamber view. G, T2 mapping showing elevated T2 in the anteroseptal wall in 3-chamber view. F, Postcontrast SSFP image showing pericardial effusion in short axis view. I-K, Athlete C with clinical myocarditis and chest pain, dyspnea, abnormal ECG, normal echocardiogram, and normal troponin findings. I, T2 mapping showing elevated T2 in the lateral wall short axis view. J, Postcontrast SSFP images showing contrast uptake in midlateral wall in short axis view. K, LGE in the epicardial midlateral wall in short axis view. L-N, Athlete D with clinical myocarditis, chest pain, abnormal ECG, echocardiogram, and troponin findings. L, T1 mapping showing elevated native T1 in midlateral wall in short axis view. M, T2 mapping showing elevated T2 in the midlateral wall in short axis view. N, LGE in the epicardial midlateral wall in short axis view. IR indicates inferior right view; IRP, inferior, right, posterior view; PLI, posterior, left, inferior view; SL, superior left view; SLA, superior, left, anterior view. Image courtesy of JAMA Cardiol. Published online May 27, 2021. doi:10.1001/jamacardio.2021.2065

News | Cardiac Imaging | June 15, 2021
June 15, 2021 — A...
Rensselaer algorithm can identify risk of cardiovascular disease using lung cancer scan #CT
News | Computed Tomography (CT) | June 14, 2021
June 14, 2021 — Heart disease and cancer are the ...
A new imaging technique has the potential to detect neurological disorders — such as Alzheimer's disease — at their earliest stages, enabling physicians to diagnose and treat patients more quickly. Termed super-resolution, the imaging methodology combines position emission tomography (PET) with an external motion tracking device to create highly detailed images of the brain.

Result of the Hoffman brain phantom study. Top row: same PET slice reconstructed with A) 2mm static OSEM, B) 1mm static OSEM, C) proposed SR method and D) corresponding CT slice (note that the CT image can be treated as a high-resolution reference). Middle row: zoom on region of interest for corresponding images. Bottom row: Line profiles for corresponding data. Image created by Y Chemli, et al., Gordon Center for Medical Imaging: Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA.

News | PET Imaging | June 14, 2021
June 14, 2021 — A new imaging technique has the potential to detect neurological disorders — such as...
Prediction performance of DL compared to quantitative measures and Kaplan-Meier curves for quartiles of DL. Image created by Singh et al., Cedars-Sinai Medical Center, Los Angeles, CA.

Prediction performance of DL compared to quantitative measures and Kaplan-Meier curves for quartiles of DL. Image created by Singh et al., Cedars-Sinai Medical Center, Los Angeles, CA.

News | SPECT Imaging | June 14, 2021
June 14, 2021 — An advanced artificial i...
Accuray Incorporated announced the company has received CE Mark certification for its ClearRT helical fan-beam kVCT imaging capability.
News | Radiation Therapy | June 11, 2021
June 11, 2021 — Accuray Incorporated announced the company has received CE Mark certification for its...
The new X-ray scanner can provide detailed information about the internal makeup of rocks, which could be useful for archaeologists studying fossils or miners making decisions about which ore to use in their extraction facilities. Image courtesy of Joel Greenberg, Duke University

The new X-ray scanner can provide detailed information about the internal makeup of rocks, which could be useful for archaeologists studying fossils or miners making decisions about which ore to use in their extraction facilities. Image courtesy of Joel Greenberg, Duke University

News | X-Ray | June 10, 2021
June 10, 2021 — Engineers at Duke University have demonstrated a prot
News | PET-CT | June 10, 2021
June 10, 2021 — Bringing the power of...