CheXbert: Radiologist-level Automated Radiology Report Labeler using Deep Learning

Stanford Reference:

20-295

Abstract

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

Applications

Radiology report labeling of medical conditions from free text radiology reports
Aid in development of an automatic chest x-ray imaging model

Advantages

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

Publications

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

Innovators & Portfolio

Saahil Jain
Pranav Rajpurkar more technologies from Pranav Rajpurkar »
Akshay Smit

Date Released

10/23/2020 12:00

Licensing Contact

Imelda Oropeza, Senior Licensing Manager, Physcial Sciences
650-725-9039 (Business)
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Related Keywords

x-Ray analysis MD: imaging: x-ray healthcare: X-Ray healthcare: Radiology Information Systems MD: imaging: software

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