Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers, a novel deep learning model, namely Multi-scale Lesion-aware Attention Networks (MLANet), is proposed for diagnosis of pneumoconiosis, staging of pneumoconiosis, and screening of stage I pneumoconiosis. A series of indicators including area under the receiver operating characteristic curve, accuracy, recall, precision, and F1 score were used to comprehensively evaluate the performance of the model. The results show that the MLANet model can effectively improve the consistency and efficiency of pneumoconiosis diagnosis. The accuracy of the MLANet model for pneumoconiosis diagnosis on the internal test set, external validation set, and prospective test set reached 97.87%, 98.03%, and 95.40%, respectively, which was close to the level of qualified radiologists. Moreover, the model can effectively screen stage I pneumoconiosis with an accuracy of 97.16%, a recall of 98.25, a precision of 93.42%, and an F1 score of 95.59%, respectively. The built model performs better than the other four classification models. It is expected to be applied in clinical work to realize the automated diagnosis of pneumoconiosis digital chest radiographs, which is of great significance for individualized early prevention and treatment.

Construction and alidation of a severity prediction model for acute pancreatitis based on CT severity index: A retrospective case-control study.

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369-3.504), BUN (OR = 1.378, 95%CI:1.026-1.959), NLR (OR = 1.370, 95%CI:1.016-1.906), D-D (OR = 1.500, 95%CI:1.112-2.110), and Ascites (OR = 5.517, 95%CI:1.217-2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians.

Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network.

BACKGROUND AND OBJECTIVE: The progressive worsening of pneumoconiosis will ensue a hazardous physical condition in patients. This study details the differential diagnosis of the pneumoconiosis stage, by employing computed tomography (CT) texture analysis, based on U-Net neural network. METHODS: The pneumoconiosis location from 92 patients at various stages was extracted by U-Net neural network. Mazda software was employed to analyze the texture features. Three dimensionality reduction methods set the best texture parameters. We applied four methods of the B11 module to analyze the selected texture parameters and calculate the misclassified rate (MCR). Finally, the receiver operating characteristic curve (ROC) of the texture parameters was analyzed, and the texture parameters with diagnostic efficiency were evaluated by calculating the area under curve (AUC). RESULTS: The original film was processed by Gaussian and Laplace filters for a better display of the segmented area of pneumoconiosis in all stages. The MCR value obtained by the NDA analysis method under the MI dimension reduction method was the lowest, at 10.87%. In the filtered texture feature parameters, the best AUC was 0.821. CONCLUSIONS: CT texture analysis based on the U-Net neural network can be used to identify the staging of pneumoconiosis.

High-resolution computed tomography diagnosis of pneumoconiosis complicated with pulmonary tuberculosis based on cascading deep supervision U-Net.

OBJECTIVE: Pulmonary tuberculosis can promote pneumoconiosis deterioration, leading to higher mortality. This study aims to explore the diagnostic value of the cascading deep supervision U-Net (CSNet) model in pneumoconiosis complicated with pulmonary tuberculosis. METHODS: A total of 162 patients with pneumoconiosis treated in our hospital were collected as the research objects. Patients were randomly divided into a training set (n = 113) and a test set (n = 49) in proportion (7:3). Based on the high-resolution computed tomography (HRCT), the traditional U-Net, supervision U-Net (SNet), and CSNet prediction models were constructed. Dice similarity coefficients, precision, recall, volumetric overlap error, and relative volume difference were used to evaluate the segmentation model. The area under the receiver operating characteristic curve (AUC) value represents the prediction efficiency of the model. RESULTS: There were no statistically significant differences in gender, age, number of positive patients, and dust contact time between patients in the training set and test set (P > 0.05). The segmentation results of CSNet are better than the traditional U-Net model and the SNet model. The AUC value of the CSNet model was 0.947 (95% CI: 0.900∼0.994), which was higher than the traditional U-Net model. CONCLUSION: The CSNet based on chest HRCT proposed in this study is superior to the traditional U-Net segmentation method in segmenting pneumoconiosis complicated with pulmonary tuberculosis. It has good prediction efficiency and can provide more clinical diagnostic value.

Intelligent Image Diagnosis of Pneumoconiosis Based on Wavelet Transform-Derived Texture Features.

Objective: Early diagnosis and treatment of occupational pneumoconiosis can delay the development of the disease. This study is aimed at investigating the intelligent diagnosis of occupational pneumoconiosis by wavelet transform-derived entropy. Method: From June 2013 to June 2020, the high KV digital radiographs (DR) and computed tomography (CT) images from a total of 60 patients with occupational pneumoconiosis in our department were selected. The wavelet transform-derived texture features were extracted from all images, and the decision tree was used for feature selection. The support vector machines (SVM) with three kernel functions were selected to classify the two kinds of images, and their diagnostic efficiency was compared. Result: After eight times of wavelet decomposition, eight wavelet entropy texture features (feature set) were extracted, and six were selected to form the feature subset. The classification effect of linear kernel function SVM is better than those of other functions, with an accuracy of 84.2%. The diagnostic values of DR and CT for occupational pneumoconiosis were the same (kappa = 0.737, P < 0.001). The detection rate of CT for stage I of occupational pneumoconiosis was significantly higher than that of DR (P = 0.031). Conclusion: It is helpful to improve the early diagnosis level of pneumoconiosis by using SVM to make an intelligent diagnosis based on the wavelet entropy.