RSNA Pulmonary Embolism Detection Challenge (2020)

The 2020 RSNA Pulmonary Embolism Detection Challenge invited researchers to develop machine-learning algorithms to detect and characterize instances of pulmonary embolism (PE) on chest CT studies. The competition, conducted in collaboration with the Society of Thoracic Radiology (STR), involved creating the largest publicly available annotated PE dataset, comprised of more than 12,000 CT studies. Imaging data was contributed by five international research centers and labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists. For the first time in an RSNA data challenge, the rules required competitors to submit and run their code in a standard shared environment, producing simpler, more readily usable models.

Artificial intelligence (AI) is a critical tool for radiologists in PE detection

PE is among the most fatal cardiovascular diseases, causing 60,000 to 100,000 deaths annually in the United States. Patients exhibit symptoms that are common to other diseases and rapid radiologic diagnosis is often critical to care decisions. This challenge demonstrates how machine learning can aid in more effective patient management and treatment by allowing radiologists to more accurately identify PE cases.

Of the 784 teams from around the world who took part in the challenge, 10 teams with the best scoring submissions will be recognized in a presentation during RSNA 2020. In recognition of the competition’s public value, the winning teams will share a total of $30,000 in prize money, provided by Kaggle.

RSNA PE Detection Challenge: Winning entries

Team name Video Solution
Guanshuo Xu Video Solution
HIGH D-DIMER Video Solution
VinBigData-Medical Imaging Video Solution
kazumax Video Solution
deepread.ai Video Solution
OsciiArt Video Solution
yuval reina Video Solution
[Aillis] Yuji + Jan + yama Video Solution
shimacha Video Solution
OrKatz Video Solution
Team name: Guanshuo Xu
Video: Video
Solution: Solution
Team name: HIGH D-DIMER
Video: Video
Solution: Solution
Team name: VinBigData-Medical Imaging
Video: Video
Solution: Solution
Team name: kazumax
Video: Video
Solution: Solution
Team name: deepread.ai
Video: Video
Solution: Solution
Team name: OsciiArt
Video: Video
Solution: Solution
Team name: yuval reina
Video: Video
Solution: Solution
Team name: [Aillis] Yuji + Jan + yama
Video: Video
Solution: Solution
Team name: shimacha
Video: Video
Solution: Solution
Team name: OrKatz
Video: Video
Solution: Solution

Access the PE Detection Challenge results on the Kaggle website.

Access results


2020 Educational Merit Award

The Educational Merit Award, newly created for 2020, is a distinction to recognize a winner from among the top 10 teams whose entry is deemed outstanding in the clarity, completeness, organization and efficiency of its submitted code.

The 2020 Educational Merit Award was presented to:

Ian Pan, MD
(Team name: HIGH D-DIMER)

Acknowledgments

Research publications for further reading

E Colak, FC Kitamura, SB Hobbs, et al. The RSNA Pulmonary Embolism CT Dataset. Radiology: Artificial Intelligence 2021;3:2.