The American College of Radiology (ACR) and Society for Imaging Informatics in Medicine (SIIM) revealed the winners of their machine learning challenge during a ceremony Sept. 24 at SIIM’s Conference on Machine Intelligence in Medical Imaging.
More than 350 teams submitted results as part of the SIIM-ACR Pneumothorax Detection and Localization Challenge and were required to create algorithms to prioritize patients for quick review and treatment. Participants used a publicly available National Institutes of Health (NIH) chest x-ray dataset.
The top 10 winners included:
1. [dsmlkz] sneddy
2. X5
3. bestfitting
4. [ods.ai] amirassov
5. earhian
6. xknife
7. See & Eduardo
8. Ian Pan & Felipe Kitamura
9. [ods.ai] Scizzo
10. [ods.ai] Yury & Konstantin
“SIIM is very pleased to have cooperated with the ACR, Google, Kaggle and the Society of Thoracic Radiology in hosting this challenge,” said Steve Langer, PhD, informatics physicist and radiology imaging architect at Mayo Clinic, and a co-chair of the SIIM Machine Learning Committee, in an ACR news release. “In addition to the medical and data science aspects, SIIM introduced the use of FHIR and DICOMweb in a medical imaging data challenge for the first time in Kaggle’s history, as those API’s are key in moving AI tools into clinical production.”
Matt joined Chicago’s TriMed team in 2018 covering all areas of health imaging after two years reporting on the hospital field. He holds a bachelor’s in English from UIC, and enjoys a good cup of coffee and an interesting documentary.