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Erschienen in:
2018 | OriginalPaper | Buchkapitel
TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays
verfasst von : Jonathan Laserson, Christine Dan Lantsman, Michal Cohen-Sfady, Itamar Tamir, Eli Goz, Chen Brestel, Shir Bar, Maya Atar, Eldad Elnekave
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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Abstract
The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.