Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage / 中华劳动卫生职业病杂志

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.

Anthracosis and anthracofibrosis

To define the clinical, radiographic, and bronchoscopic features, and to describe the occupations of the largest group of patients with anthracosis. All patients who underwent flexible bronchoscopy at 2 Iranian hospitals [Imam Hospital [Tehran], and Tohid Hospital [Sanandaj]], Iran, between April 1982 and June 2006 were considered for inclusion in the study. The demographic data, clinical, and radiographic findings of anthracotic and anthracofibrotic patients were recorded. Of the 14300 patients, 487 cases of simple anthracosis, and 291 of anthracofibrosis were found. A total of 98.4% female patients were housewives, and 86.4% lived in rural areas. Of the male patients, 40.6% were farmers, 29.6% were manual workers, and 7.5% were miners. Of these, 96% of patients had abnormal chest radiography. On bronchoscopic examination, bilateral bronchial involvement was found in 62.5% of the patients. The condition was confined to the trachea in 0.38% of patients, the bronchi involved were the main bronchus in 37%, the lobar bronchi in 83.2%, and segmental bronchi in 35%. Bronchial narrowing and obstruction was observed in 37.4% of the patients. Anthracosis and anthracofibrosis are neglected conditions that are a common finding on routine bronchoscopic examination. Given the demographic findings, and a review of other reports from developing countries, exposure to combustion of biomass fuel in rural areas is a possible risk factor