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.

Value of pulmonary ventilation score in evaluating extravascular lung water of patients with acute respiratory distress syndrome / 第二军医大学学报

Objective To discuss the value of pulmonary ventilation score in evaluating the extravascular lung water (EVLW) of patients with acute respiratory distress syndrome (ARDS). Methods We retrospectively collected the clinical data of 32 patients with ARDS, who were treated in the Department of Critical Care Medicine of Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine from Jun. 2015 to Feb. 2017 and improved within 7 days. The total pulmonary ventilation score, extravascular lung water index (EVLWI), oxygenation index (PaO2/FiO2), and central venous pressure (CVP) of patients at admission and after treatment for 7 d were recorded. The correlations between total pulmonary ventilation score and acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score, EVLWI, oxygenation index and CVP were analyzed. Results The APACHE Ⅱ score, total pulmonary ventilation score and EVLWI of the ARDS patients after treatment for 7 d were significantly decreased compared with those at admission, and the oxygenation index was significantly increased (all P0.01). The pulmonary ventilation score was positively correlated with the APACHE Ⅱ score, EVLWI and CVP (r＝0.95, 0.95, 0.64; all P0.01), and was negatively correlated with the oxygenation index (r＝－0.94, P0.01). Conclusion Pulmonary ventilation score can effectively evaluate the EVLW of patients with ARDS, and can be used as an effective supplement for EVLW monitoring in patients with ARDS in addition to pulse indicator continous cadiac output.