Deep learning to convert unstructured CT pulmonary angiography reports into structured reports
| Autor: | James G. Ravenel, Pooyan Sahbaee, Adam Spandorfer, U. Joseph Schoepf, Cody Branch, John W. Nance, Puneet Sharma |
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| Rok vydání: | 2019 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
Artificial intelligence Computed Tomography Angiography Computer science lcsh:R895-920 education Pulmonary Artery computer.software_genre Convolutional neural network Medical Records 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep Learning 0302 clinical medicine Machine learning Pulmonary angiography Humans Radiology Nuclear Medicine and imaging Typographical error Retrospective Studies Neuroradiology Statement (computer science) Ct pulmonary angiography business.industry Natural language processing Deep learning 030220 oncology & carcinogenesis Test set Original Article Tomography (x-ray computed) business computer Algorithms Structured reporting |
| Zdroj: | European Radiology Experimental European Radiology Experimental, Vol 3, Iss 1, Pp 1-8 (2019) |
| ISSN: | 2509-9280 |
| Popis: | Background Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed to convert unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports. Methods A self-supervised convolutional neural network-based algorithm was trained on a dataset of 475 manually structured CTPA reports. Labels for individual statements included “pulmonary arteries,” “lungs and airways,” “pleura,” “mediastinum and lymph nodes,” “cardiovascular,” “soft tissues and bones,” “upper abdomen,” and “lines/tubes.” The algorithm was applied to a test set of 400 unstructured CTPA reports, generating a predicted label for each statement, which was evaluated by two independent observers. Per-statement accuracy was calculated based on strict criteria (algorithm label counted as correct if the statement unequivocally contained content only related to that particular label) and a modified criteria, accounting for problematic statements, including typographical errors, statements that did not fit well into the classification scheme, statements containing content for multiple labels, etc. Results Of the 4,157 statements, 3,806 (91.6%) and 3,986 (95.9%) were correctly labeled by the algorithm using strict and modified criteria, respectively, while 274 (6.6%) were problematic for the manual observers to label, the majority of which (n = 173) were due to more than one section being included in one statement. Conclusion This algorithm showed high accuracy in converting free-text findings into structured reports, which could improve communication between radiologists and clinicians without loss of productivity and provide more structured data for research/data mining applications. |
| Databáze: | OpenAIRE |
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