The RSNA in collaboration with the Society for Thoracic Radiology (STR) has announced the winners of the Pulmonary Embolism Detection Challenge.
The RSNA-STR Pulmonary Detection Challenge is the fourth-annual iteration of the RSNA artificial intelligence (AI) challenge. Teams were tasked with creating a machine-learning algorithm that can detect and characterize pulmonary embolism from a dataset of more than 12,000 CT scans from five international research centers.
A total of 784 teams from around the world participated in this year's AI event. The top 10 winning teams are as follows:
- Guanshuo Xu
- HIGH D-DIMER
- VinBigData-Medical Imaging
- kazumax
- deepread.ai
- OsciiArt
- yuval reina
- [Aillis] Yuji + Jan + yama
- shimacha
- OrKatz
In addition, Dr. Ian Pan from HIGH D-DIMER won the inaugural 2020 Education Merit Award. The newly created award recognizes an individual from the top 10 teams whose code is outstanding for its completeness, organization, clarity, and efficiency.
The winning teams will share $30,000 in prize money from Kaggle. They will also be recognized during the virtual RSNA meeting, which is scheduled to take place from November 29 to December 5.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
