Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network.
Qing-Qing ZhouWen TangJiashuo WangZhang-Chun HuZi-Yi XiaRongguo ZhangXinyi FanWei YongXin-Dao YinBing ZhangHong ZhangPublished in: European radiology (2020)
• The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.
Keyphrases
- convolutional neural network
- deep learning
- artificial intelligence
- image quality
- dual energy
- computed tomography
- machine learning
- contrast enhanced
- end stage renal disease
- clinical practice
- chronic kidney disease
- ejection fraction
- newly diagnosed
- positron emission tomography
- peritoneal dialysis
- magnetic resonance imaging
- health information
- magnetic resonance
- healthcare
- label free