Feasibility of constructing the intelligent detection model for foreign bodies on chest X-ray based on Faster R-convolutional neural network
10.3760/cma.j.cn112149-20211231-01167
- VernacularTitle:基于Faster R卷积神经网络构建胸部X线片异物智能检测模型的可行性研究
- Author:
Yu MENG
1
;
Zhicheng MA
;
Jingru RUAN
;
Yang GAO
;
Bailin YANG
;
Linyang HE
;
Xiangyang GONG
Author Information
1. 浙江省人民医院 杭州医学院附属人民医院康复医学中心放射科，杭州 310014
- Keywords:
X-ray;
Chest;
Foreign body;
Quality control;
Deep learning
- From:
Chinese Journal of Radiology
2022;56(12):1359-1364
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct an intelligent foreign bodies detection model based on Faster R-convolutional neural network in posterior-anterior chest X-ray and evaluate the performance of the model.Methods:Totally 5 567 adult posterior-anterior DR chest radiographs from Zhejiang Provincial People′s Hospital and Chun′an County People′s Hospital from June 2019 to March 2020, with 4 247 foreign body-containing chest radiographs were analyzed retrospectively. All data were randomly divided into training set (2 911 foreign body-containing), validation set ( n=1 456, 733 foreign body-containing, 723 free of foreign body) and testing set ( n=1 200, 603 foreign body-containing, 597 free of foreign body). The reference gold standard was set as the results of each chest radiography with foreign body annotated by two radiology residents and reviewed and corrected by a senior radiographer. The receiver operating characteristic (ROC) curve and the area under the curve were used to analyze the efficiency of the deep learning model to distinguish the presence or absence of foreign bodies on chest radiography in the testing set. The precision-recall curve and mean precision (mAP) were used to analyze the stability of the model at different levels. Finally, the influence of different locations, patient gender, and patient age on the foreign body recall of the deep learning model were analyzed. Results:In the testing set, the sensitivity of the deep learning model in diagnosing whether chest radiograph contained foreign bodies was 93.2%(562/603), the specificity was 92.6%(553/597), and the F1 score was 0.94. The area under the ROC curve was 0.97, and the mAP value was 0.69. For foreign bodies in different locations, the recall rates of foreign bodies in lung field and outside lung field were 91.2% (674/739) and 89.0% (1 411/1 585), respectively. For different genders, the recall rates for male and female foreign body detection were 87.3% (337/386) and 90.0%(1 745/1 938), respectively. For different age ranges, the recall rate of foreign body detection was 92.5% (1 041/1 126) for 18-38 years old, 89.7%(505/563) for 39-58 years old, 83.5%(335/401) for 59-78 years old and 85.9% (201/234) for patients ≥79 years old.Conclusion:The constructed deep learning-based foreign body detection model for adult posterior-anterior chest X-ray provides high sensitivity and stability, which can identify foreign bodies in chest radiography quickly and accurately.
