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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Xu, Xiaowei Qin, Lu Ding, Lingling Wang, Chunjuan Wang, Meng Li, Zixiao Li, Jiao |
| Abstract | Background Medical imaging reports play an important role in communication of diagnostic information between radiologists and clinicians. Head magnetic resonance imaging (MRI) reports can provide evidence that is widely used in the diagnosis and treatment of ischaemic stroke. The high-signal regions of diffusion-weighted imaging (DWI) images in MRI reports are key evidence. Correctly identifying high-signal regions of DWI images is helpful for the treatment of ischaemic stroke patients. Since most of the multiple signals recorded in head MRI reports appear in the same part, it is challenging to identify high-signal regions of DWI images from MRI reports. Methods We developed a deep learning model to automatically identify high-signal regions of DWI images from head MRI reports. We proposed a fine-grained entity typing model based on machine reading comprehension that transformed the traditional two-step fine-grained entity typing task into a question-answering task. Results To prove the validity of the model proposed, we compared it with the fine-grained entity typing model, of which the F1 measure was 5.9% and 3.2% higher than the F1 measures of the models based on LSTM and BERT, respectively. Conclusion In this study, we explore the automatic identification of high-signal regions of DWI images from the description part of a head MRI report. We transformed the identification of high-signal regions of DWI images to an FET task and proposed an MRC-FET model. Compared with the traditional two-step FET method, the model we proposed not only simplifies the task but also has better performance. The comparable result shows that the work in this study can contribute to improving the clinical decision support system. |
| Related Links | https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-022-02012-3.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726947 |
| DOI | 10.1186/s12911-022-02012-3 |
| Journal | BMC Medical Informatics and Decision Making |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2022-10-20 |
| Access Restriction | Open |
| Subject Keyword | Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Ischaemic stroke High-Signal intensity regions DWI MRI reports Fine-grained entity typing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications Health Policy |
| Journal Impact Factor | 3.3/2023 |
| 5-Year Journal Impact Factor | 3.9/2023 |
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