Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.

Solid-state ionics method fabricated centimeter level CuAu alloy nanowires: Application in SERS.

CuAu alloy nanowires were prepared by a solid-state ionics method under a direct current electric field (DCEF) using fast ionic conductor Rb4Cu16Cl13I7 films. The surface morphology, chemical composition and crystal structures of the CuAu alloy nanowires were characterized by scanning electron microscopy (SEM), energy dispersive spectrometer (EDS) and X-ray diffraction (XRD), respectively. Raman enhancement performance of the CuAu alloy nanowires substrates was detected by Rhodamine 6G (R6G) aqueous solutions as probe molecules. Long-range disorder and short-range order CuAu alloy nanowires with the length of 1 cm were prepared by a solid-state ionics method. The nanowires were bamboo-shaped and the diameters of nanowires ranged from 40 to 100nm. The molar ratio of Cu to Au is 16:1. The crystal structure of the CuAu alloy nanowires is crystallized. A part of Cu and Au formed Au3Cu alloy structure. The limiting concentrations of R6G for the prepared CuAu alloy nanowires SERS substrates is 10-15mol/L.