| Autor: |
LI Ze-hao, LIU Ning-guo, DONG He-wen, et al. |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Fayixue Zazhi, Vol 37, Iss 4, Pp 546-554 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1004-5619 |
| DOI: |
10.12116/j.issn.1004-5619.2021.410503 |
| Popis: |
In the field of forensic medicine, diagnosis of sudden cardiac death is limited by subjective factors and manual measurement methods, so some parameters may have estimation deviation or measurement deviation. As postmortem CT imaging plays a more and more important role in the appraisal of cause of death and cardiopathology research, the application of deep learning such as artificial intelligence technology to analyze vast amounts of cardiac imaging data has provided a possibility for forensic identification and scientific research workers to conduct precise diagnosis and quantitative analysis of cardiac diseases. This article summarizes the main researches on deep learning in the field of cardiac imaging in recent years, and proposes a feasible development direction for the application of deep learning in the virtual anatomy of sudden cardiac death at present. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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