Investigation on the Detection Capability of Artificial Intelligence for Pulmonary Nodules
Using Dual-energy Fusions Image
SONG Dongdong1，ZHU Xiaoming1，ZHU Lijuan1，GU Jun2，WU Jianlin1，ZHANG Qing1
1. Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian Liaoning 116001, China|
2. Institute of Global Clinical Research Collaboration, Infervision Technology Co., Ltd, Beijing 100025, China
Abstract：Objective This study explores the pulmonary nodules detection capability of an artificial intelligence diagnostic system
using dual-energy CT fusion with 120 kVp image. Methods 381 lung cancer screening patients underwent dual-energy CT scans in
our hospital were prospectively enrolled in this study and randomly divided into two groups: group A (120 kVp single energy scan,
183 cases) and group B (100/Sn 140 kVp dual-energy scan, 198 cases). Both groups were treated with CareDose 4D technology.
Volumetric CT Dose index (CTDIvol) value and dose-length Product (DLP) value were recorded. AI software was used to detect the
nodules in the image, and the size, location and type of nodules (solid and subsolid) were recorded. By comparing with the golden
standard, total number of detected nodules was calculated, as well as nodules with different size (≥4mm and ＜4mm), nodules with
different density (solid and subsolid), and TPF, FPF, FNF. Finally, the sensitivity, precision and false positive rate of the AI system
were obtained. Difference comparision studies were conducted, and P<0.05 for the difference was statistically significant. Results
The sensitivity of pulmonary nodules detection in group B was higher than that in group A (P<0.05), and the radiation dose was lower
than that in group A (P<0.05). Meanwhile, the total false positive rate of pulmonary nodules detection in group B was lower than that
in group A. Conclusion It is concluded from this study that dual-energy fusion 120 kVp image is more effective than single-source
scan 120 kVp image in detection of lung nodules, and its radiation dose is lower, which is more suitable for detection of lung cancer
by AI software.
宋冬冬1，朱晓明1，朱丽娟1，顾俊2，伍建林1，张清1. 双能CT融合图像在人工智能肺结节筛查中检测效能的探索研究[J]. 中国医疗设备, 2021, 36(2): 73-76.
SONG Dongdong1，ZHU Xiaoming1，ZHU Lijuan1，GU Jun2，WU Jianlin1，ZHANG Qing1. Investigation on the Detection Capability of Artificial Intelligence for Pulmonary Nodules
Using Dual-energy Fusions Image. China Medical Devices, 2021, 36(2): 73-76.
