Objective: To investigate hearing loss status of blasters, drillers mechanics and so on in underground mining, and put forward suggestion diagnosis of occupational explosive deafness and occupational deafness. Methods: Underground excavation workers in a metal mine were recruited in this study, those with a history of ear disease and non-occupational deafness were all excluded. Finally, the features of pure tone audiometry of 459 noise-exposed workers were analyzed. Results: High-frequency hearing loss occurred on 351workers and the positive detection rate was 74.29%, workers who had both high-frequency and linguistic frequency hearing loss were 51 and the positive detection rate was 11.11%. The positive detection of high-frequency hearing loss in right ear (χ²=9.427 and P= 0.024) and in left ear (χ²=14.375, P=0.002) was significantly different between different exposure age groups. The positive detection of high-frequency hearing loss of driving group was the highest, followed by blasting group, mining group and machine repair group. The characteristics of the hearing loss caused by drilling noise of the blasting workers with no accident occurred were in line with that of noise-induced hearing loss. Conclusion The diagnosis grading should be carried out according to the diagnostic criteria of occupational noise-induced deafness for the employees who engaged in the blasting operation with no record of blast accident.

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.