TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Eli Goz 1 Jonathan Laserson 1 Christine Dan Lantsman 2 Michal Cohen-Sfadi 1 Itamar Tamir 3 Chen Brestel 1 Shir Bar 4 Maya Atar 5 Eldad Elnekave 1
1Zebra Medical Vision LTD, Israel
2Sheba Medical Center and Tel Aviv University, Israel
3Rabin Medical Center, Israel
4Technion - Israel Institute of Technology, Israel
5Ben Gurion University, Israel

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.

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