Application value of computer-aided diagnosis in diagnosing pneumoconiosis
10.11763/j.issn.2095-2619.2020.04.009
- Author:
Zheng WANG
1
;
Qingjun QIAN
1
;
Jianfang ZHANG
1
;
Caihong DUO
1
;
Xiaopeng WEI
1
;
Min ZHU
1
Author Information
1. National Center for Occupational Safety and Health,National Health Commission Beijing 102300, China
- Publication Type:Journal Article
- Keywords:
Pneumoconiosis;
Computer;
Aided diagnosis;
Receiver operating characteristic curve;
Sensitivity;
Specificity;
Digital radiography
- From:
China Occupational Medicine
2020;47(04):428-431
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE: To explore the application value of computer-aided diagnosis technology based on deep residual network in the diagnosis of occupational pneumoconiosis(hereinafter referred to as pneumoconiosis). METHODS: A total of 5 424 digital radiography chest images were collected from occupational health examiners using a convenient sampling method.These images were used to establish a data set. After training with the data set, the pneumoconiosis computer-aided diagnosis system was used to independently diagnose the test set images(50 positive and negative cases each) and output a positive probability value. Six diagnostic physicians with varied ages and different experiences performed independent diagnosis on the test set and assisted diagnosis with reference to computer results. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve(AUC) value, sensitivity, and specificity.The Kappa consistency test was used to evaluate the diagnostic consistency. RESULTS: The AUC value, sensitivity, specificity, and Kappa value of pneumoconiosis diagnosis increased after using computer-aided diagnosis. The sensitivity increased from 0.74 to 0.85(P<0.05)and the Kappa value increased from 0.64 to 0.79(P<0.05). The AUC value increased from 0.90 to 0.95, and the specificity increased from 0.89 to 0.94, but there were no statistical difference(P<0.05). CONCLUSION: Computer-aided diagnosis can improve the sensitivity and consistency of pneumoconiosis screening and reduce the differences in diagnosis among physicians.
