RSNA announced the official results of its latest artificial intelligence (AI) challenge Monday during a presentation in the RSNA AI Theater.
The RSNA Intracranial Hemorrhage Detection and Classification Challenge required teams to develop algorithms that can identify and classify subtypes of hemorrhages on head CT scans. The data set, which comprises more than 25,000 head CT scans contributed by several research institutions, is the first multiplanar dataset used in an RSNA AI Challenge.
The Machine Learning Steering Subcommittee and the Machine Learning Data Standards Subcommittee worked with volunteer specialists from the American Society of Neuroradiology (ASNR) to label these exams for the presence of five subtypes of intracranial hemorrhage — an effort of unprecedented scope in the radiology community.
"The volunteers who contributed data and who worked on annotating the dataset have created a resource of tremendous value for imaging research," said Charles E. Kahn, Jr., MD, MS, chair of the RSNA Radiology Informatics Committee (RIC) and Radiology: AI editor.
The award-winning teams in the RSNA Intracranial Hemorrhage Detection and Classification Challenge are SeuTao, NoBrainer, takuoko, GZ, Keep Digging Gold, BRAINSCAN.AI, Big Head, 賞金で焼肉, Mind Blowers and VinBDI.MedicalImagingTeam.
"The challenge demonstrates the increasing sophistication of the imaging AI research community and the real potential of this technology to improve the efficiency and quality of care in radiology," said Luciano M. Prevedello, MD, MPH, chair of the Machine Learning Steering Subcommittee of the RIC.
The challenge was run on a platform provided by Kaggle, Inc. (a subsidiary of Alphabet, Inc., also the parent company of Google). Kaggle also provided $25,000 in prize money to be shared among the winning entries.
For more information on the challenge, visit RSNA.org/AI-image-challenge.