The Office of Technology Licensing was established in 1970 to transfer technologies developed at Stanford. Find out more about OTL's history, mission, staff, and statistics.
CheXbert: Radiologist-level Automated Radiology Report Labeler using Deep Learning
The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format. Previous approaches to report labeling typically rely either on sophisticated engineering based on medical domain knowledge or manual annotations by experts. CheXbert uses a novel approach to medical image report labeling that leverages recent advances in natural language processing. CheXbert is developed using labels provided by both board-certified radiologists and the previous state-of-the-art automatic labeler.
In experiments, CheXbert performed comparably to a radiologist and is able to outperform the previous best automatic labeler with statistical significance, setting a new state-of-the-art for report labeling on one of the largest datasets of chest x-rays. Accurate labeling of radiology text reports can enable high-quality training of AI-based medical imaging interpretation models.
Stage of Development
Software has been trained on free text radiology report impressions and can be used to extract conditions from new radiology reports
Radiology report labeling of medical conditions from free text radiology reports
Aid in development of an automatic chest x-ray imaging model
Outperforms the previous best radiology report labelers
with statistical significance, achieving the current state-of-the-art
Automatic and Accurate
Leverages recent advances in natural language processing (NPL)
Labeler can utilize both expert annotations and existing labelers’ outputs on radiology reports
No manual annotation or fine-tuning required for use with data from a different hospital
Unlike previous rules-based report labelers, further fine-tuning and improvement is possible with more data, if available, without coding expertise
Smit, A., Jain, S., Rajpurkar, P., Pareek, A., Ng, A. Y., & Lungren, M. P. (2020).
CheXbert: Combining Automatic Labelers and Expert Annotations for AccurateRadiology Report Labeling Using BERT.
arXiv preprint arXiv:2004.09167
Related Web Links
Innovators & Portfolio
more technologies from Pranav Rajpurkar »
Imelda Oropeza, Senior Licensing Associate
Scintillation Crystal Detection Arrays for Radiation Imaging Devices
Scatter Correction Algorithm for X-ray and Volumetric CT
Laser-driven deflection structure for charged particle beams
more technologies »
healthcare: Radiology Information Systems
AI: medical imaging
Office of Technology Licensing • 415 Broadway Street, 2nd Floor • Redwood City, CA 94063 •