Research progress of artificial intelligence and machine learning in pulmonary embolism
The pathophysiology and clinical manifestations of pulmonary embolism are complex, heterogeneous, and the disease burden is severe, and its prediction and diagnosis are of major challenges. Artificial intelligence (AI) is a field of computer science that involves the development of programs and comp...
Saved in:
| Main Authors: | Yue Li, Limin Zhang, Haoran Liu, Yanxia Li, Zhuo Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1577559/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research progress on the association between lung cancer and pulmonary embolism
by: Yi-Wen Zhang, et al.
Published: (2025-01-01) -
Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms
by: Haobo Kong, et al.
Published: (2024-12-01) -
Antithrombin activity is a significant predictor of early mortality in pulmonary embolism patients
by: Džudović Boris, et al.
Published: (2022-01-01) -
Assessing the severity of acute pulmonary embolism: A review of epidemiological, clinical, biological, and CT angiography parameters
by: Jean-Pierre Tshungu Muteleshi, et al.
Published: (2025-07-01) -
Predictive value of the simplified pulmonary embolism severity index combined with serum uric acid and neutrophil percentage in the short-term prognosis of lung cancer patients with acute pulmonary embolism
by: YAN Qiumei, ZHANG Xing, LI Yuying
Published: (2025-02-01)