DISCLAIMER: This a RESEARCH USE ONLY tool. Its purpose is to help clinicians and researchers explore the value of AI in medical imaging. These AI models are not regulated or validated and only for suggestion purposes.
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Pneumonia detection

Detect and localize pneumonia on chest radiographs, automatically

Your own data

Cost: Free during Beta

Probabilistically localize suspected foci of pneumonia on frontal chest radiographs.

Intended Use

Any PA/AP Chest Radiograph

Limitations

Does not consider prior information for the same patient as part of a prior prior likelhood.

Information on training data

Trained on 22K and tested on 4K frontal radiographs from a public dataset. Two pneumonia models were trained: (a) without and (b) with thoracic cavity annotations.

Model performance metrics

Area under the ROC curve (AUC) for detection of pneumonia was 0.861 and improved to 0.906 with concurrent training with the thoracic cavity annotation.

Performance Curves

Pneumonia detection ROC curve on public dataset od 3985 frontal Chest Xrays.

See reference publication for more information.

Model History

Version v0.9


UCSD

The University California, San Diego is one of the world's leading public research universities, located in beautiful La Jolla, California


Brian Hurt

Albert Hsiao