RSNA Announces Intracranial Aneurysm Detection AI Challenge Results

OAK BROOK, Ill. (Nov. 25, 2025) – The Radiological Society of North America (RSNA) has announced the results of the 2025 RSNA Intracranial Aneurysm Detection AI Challenge. 

The challenge, developed in collaboration with the American Society of Neuroradiology (ASNR), the European Society of Neuroradiology (ESNR) and the Society of Neurointerventional Surgery (SNIS), focused on AI-assisted detection and localization of intracranial aneurysms. 

“AI challenges like this one are essential for advancing the field,” said Jeff Rudie, M.D., Ph.D., co-leader of the challenge planning task force and member of the RSNA Radiology Informatics Council’s Artificial Intelligence Committee. “They create structured opportunities for researchers worldwide to solve clinically meaningful problems, accelerate innovation and build tools that can eventually improve patient care.”

The winning teams in the RSNA Intracranial Aneurysm Detection AI Challenge are:

1. tomoon33
2. BraveCoWCoW
3. BTYND
4. Harshit Sheoran
5. more CV challenge pls
6. Ian, Theo & Bartley
7. MIC-DKFZ
8. Konni
9. Vibes and Genius Trade-Off

The Educational Merit Award is a distinction to recognize a winner from among the top nine teams whose entry is deemed outstanding in clarity, completeness, organization and efficiency of its submitted code. Two teams were selected for the 2025 Educational Merit Award: Ian, Theo & Bartley and MIC-DKFZ.

Intracranial aneurysms affect an estimated 3.2% of the global population, according to studies cited by the U.S. National Library of Medicine. Alarmingly, up to 50% of intracranial aneurysms are first identified only after rupture—an event associated with high rates of serious complications and death.

Accurate and timely detection of aneurysms is critical for guiding treatment and preventing rupture. When identified early, patients can undergo monitoring or intervention that significantly reduces the risk of adverse outcomes. 

AI tools that support radiologists in detecting these often-subtle lesions have the potential to transform patient care by improving diagnostic accuracy and efficiency.

The goal of the RSNA Intracranial Aneurysm Detection AI Challenge was to develop machine learning models to detect and localize intracranial aneurysms across a variety of medical imaging modalities, including CTA, MRA and T1 post-contrast and T2-weighted MRI. 

“This challenge aimed to provide new state-of-the-art methods for automated detection of aneurysms on routine brain scans done for other purposes,” said Evan Calabrese, M.D., Ph.D., challenge planning task force co-leader, clinical neuroradiologist and assistant professor of radiology at Duke University School of Medicine in Durham, North Carolina. “This opportunistic screening approach may save lives and reduce diagnostic burden for radiologists, using the images that we already have available. It’s a win-win for patients, radiologists and health systems.”

The challenge, launched in July and hosted on a platform provided by Kaggle, Inc. (an Alphabet company), attracted 1,147 teams from around the world. 

Participants were tasked with detecting and localizing aneurysms across 13 distinct anatomical locations within the intracranial circulation. The reference dataset—meticulously curated by the RSNA challenge planning task force—included CT and MR imaging exams from 18 sites across five continents, annotated by more than 60 expert radiologists for the presence and location of aneurysms. To give competitors richer anatomical context, a subset of MRI studies also included 3D segmentations of those same 13 vascular locations, where aneurysms most commonly arise.

The competition phase concluded in October. The prize-winning solutions were then reviewed by a team of volunteer AI experts to confirm the results. The nine teams submitting the highest-scoring algorithms shared in $50,000 total prize money. 

Winners will be recognized in the AI Theater (Booth 5536), South Hall A, on Dec. 1 at 4 p.m. CT during RSNA 2025 at McCormick Place Chicago (Nov. 30 – Dec. 4, 2025). 

For more information on RSNA AI challenges, visit RSNA.org/AI-image-challenge or contact informatics@rsna.org.