
A machine-learning challenge to develop algorithms for the detection of pneumothorax on x-ray is being hosted by the American College of Radiology (ACR), the Society for Imaging Informatics in Medicine (SIIM), and the Society of Thoracic Radiology (STR), along with imaging artificial intelligence (AI) developer MD.ai.
Participants of the challenge will be tasked with developing machine-learning algorithms capable of detecting pneumothorax on chest x-rays as well as prioritizing patients for expedited review. Ahead of the competition, radiologists from SIIM and STR have used MD.ai's web-based tool to annotate the radiographs and have stored the dataset in Kaggle's cloud-based data platform.
"This Kaggle competition will result in open-source algorithms to help solve a serious healthcare problem that can lead to death if not identified and treated quickly," Dr. Bibb Allen Jr., chief medical officer of the ACR Data Science Institute, said in a statement.
The societies will launch the challenge June 26 to 28 at the 2019 SIIM meeting in Aurora, CO. They will deliver awards to the winning teams during the 2019 SIIM Conference on Machine Intelligence in Medical Imaging (C-MIMI) taking place September 22 to 23 in Austin, TX.













![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&fit=crop&h=112&q=70&w=112)




