[Study on evaluation method of circular small shadow profusion in chest CT reconstruction images of pneumoconiosis].

Objective: To select chest CT image patterns for the diagnosis of pneumoconiosis and establish a method for determining the profusion of circular small shadows in chest CT. Methods: In April 2021, 66 cases of occupational pneumoconiosis patients with digital radiography (DR) chest radiographs and chest CT imaging data with circular small shadow as the main manifestations were selected as the study objects. 1.5 mm and 5 mm chest CT axial images, 1 mm and 5 mm chest CT coronal multi-plane recombination (MPR) images, and 5 mm chest CT coronal maximum intensity projection (MIP) images were used to observe the different characteristics of pneumoconiosis patients, and were compared and analyzed with DR chest radiographs to establish the experimental chest CT standards. The consistency of the profusion results between the experimental chest CT standards and GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis was verified. Results: All the 66 objects were male, including 33 cases of stage Ⅰ pneumoconiosis, 17 cases of stage Ⅱ pneumoconiosis and 16 cases of stage Ⅲ pneumoconiosis. By observing five chest CT images of 66 objects, we found that chest CT images of different modes could clearly display and identify abnormal images such as small circular shadow, large shadow, small shadow aggregation, honeycomb glass shadow, flake glass shadow, uniform low-profusion glass shadow, mesh glass shadow, cable shadow, linear shadow, subpleural spinous shadow, subpleural nodules, various kinds of emphysema and lung texture distortion and fracture. Small shadow aggregation was usually accompanied by the appearance of large shadow. The vascular shadows in 5 mm CT images had good ductility, and small nodules were easy to distinguish. The coronal MIP image of 5 mm chest CT used edge enhancement technology, which was prone to small shadow fusion and fibrotic shadow fusion. The coronal MPR image of 5 mm chest CT was highly consistent with the DR chest radiographs in terms of the integrity of film reading. GBZ 70-2015 standard was used to compare the profusion of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, and the consistency test Kappa=0.64. GBZ 70-2015 standard and experimental chest CT standard were used to compare the profusion results of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, respectively, and the consistency test Kappa=0.80, with high consistency. Conclusion: 5 mm coronal MPR image is suitable for chest CT imaging in the diagnosis of pneumoconiosis. Following the selection path and method of GBZ 70-2015 profusion criterion, the established experimental chest CT standard in determining the profusion of small circular shadows in 5 mm coronal MPR images of chest CT with pneumoconiosis has a high consistency with GBZ 70-2015 standard.

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lung regions as well as inter-class similarity and intra-class variance. Compared to traditional methods, Convolutional Neural Networks-based methods have shown improved results; however, these methods are still not applicable in clinical practice due to limited performance. In some cases, limited computing resources make it impractical to develop a model using whole CXR images. To address this problem, the lung fields are divided into six zones, each zone is classified separately and the zone classification results are then aggregated into an image classification score, based on state-of-the-art. In this study, we propose a dual lesion attention network (DLA-Net) for the classification of pneumoconiosis that can extract features from affected regions in a lung. This network consists of two main components: feature extraction and feature refinement. Feature extraction uses the pre-trained Xception model as the backbone to extract semantic information. To emphasise the lesion regions and improve the feature representation capability, the feature refinement component uses a DLA module that consists of two sub modules: channel attention (CA) and spatial attention (SA). The CA module focuses on the most important channels in the feature maps extracted by the backbone model, and the SA module highlights the spatial details of the affected regions. Thus, both attention modules combine to extract discriminative and rich contextual features to improve classification performance on pneumoconiosis. Experimental results show that the proposed DLA-Net outperforms state-of-the-art methods for pneumoconiosis classification.

[A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].

Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.

Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis.

OBJECTION: The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features. METHODS: A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals. RESULTS: Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later. CONCLUSION: This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.

Asian Intensive Reader of Pneumoconiosis program: examination for certification during 2008-2020.

This study examined physicians' participation and performance in the examinations administered by the Asian Intensive Reader of Pneumoconiosis (AIR Pneumo) program from 2008 to 2020 and compared radiograph readings of physicians who passed with those who failed the examinations. Demography of the participants, participation trends, pass/fail rates, and proficiency scores were summarized; differences in reading the radiographs for pneumoconiosis of physicians who passed the examinations and those who failed were evaluated. By December 2020, 555 physicians from 20 countries had taken certification examinations; the number of participants increased in recent years. Reported background specialty training and work experience varied widely. Passing rate and mean proficiency score for participants who passed were 83.4% and 77.6 ± 9.4 in certification, and 76.8% and 88.1 ± 4.5 in recertification examinations. Compared with physicians who passed the examinations, physicians who failed tended to classify test radiographs as positive for pneumoconiosis and read a higher profusion; they likely missed large opacities and pleural plaques and had a lower accuracy in recognizing the shape of small opacities. Findings suggest that physicians who failed the examination tend to over-diagnose radiographs as positive for pneumoconiosis with higher profusion and have difficulty in correctly identifying small opacity shape.

Characterizing Lung Particulates Using Quantitative Microscopy in Coal Miners With Severe Pneumoconiosis.

CONTEXT.­: Current approaches for characterizing retained lung dust using pathologists' qualitative assessment or scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) have limitations. OBJECTIVE.­: To explore polarized light microscopy coupled with image-processing software, termed quantitative microscopy-particulate matter (QM-PM), as a tool to characterize in situ dust in lung tissue of US coal miners with progressive massive fibrosis. DESIGN.­: We developed a standardized protocol using microscopy images to characterize the in situ burden of birefringent crystalline silica/silicate particles (mineral density) and carbonaceous particles (pigment fraction). Mineral density and pigment fraction were compared with pathologists' qualitative assessments and SEM/EDS analyses. Particle features were compared between historical (born before 1930) and contemporary coal miners, who likely had different exposures following changes in mining technology. RESULTS.­: Lung tissue samples from 85 coal miners (62 historical and 23 contemporary) and 10 healthy controls were analyzed using QM-PM. Mineral density and pigment fraction measurements with QM-PM were comparable to consensus pathologists' scoring and SEM/EDS analyses. Contemporary miners had greater mineral density than historical miners (186 456 versus 63 727/mm3; P = .02) and controls (4542/mm3), consistent with higher amounts of silica/silicate dust. Contemporary and historical miners had similar particle sizes (median area, 1.00 versus 1.14 µm2; P = .46) and birefringence under polarized light (median grayscale brightness: 80.9 versus 87.6; P = .29). CONCLUSIONS.­: QM-PM reliably characterizes in situ silica/silicate and carbonaceous particles in a reproducible, automated, accessible, and time/cost/labor-efficient manner, and shows promise as a tool for understanding occupational lung pathology and targeting exposure controls.

Assessment of the Respiratory Health of Working US Coal Miners Since 2014-Radiography, Spirometry, and Symptom Assessments.

OBJECTIVE: The aim of the study is to summarize Coal Workers' Health Surveillance Program findings since 2014, focusing on prevalence of radiographic pneumoconiosis and abnormal spirometry by region. METHODS: Analysis included the most recent Coal Workers' Health Surveillance Program encounter for working miners during October 1, 2014-June 30, 2022. Central Appalachia consists of Kentucky, Virginia, and West Virginia. RESULTS: Pneumoconiosis prevalence was highest among underground miners, with 318 (6.2%) having radiographic evidence of disease. Central Appalachian miners were more likely to have evidence of pneumoconiosis (relative risk = 4.1 [3.4-5.0]) and abnormal spirometry (relative risk = 1.4 [1.2-1.6]) compared with all others. CONCLUSIONS: Central Appalachia is a hotspot for pneumoconiosis, as well as for other indicators of respiratory impairment in coal miners. Outreach for occupational respiratory health surveillance should focus on those areas most heavily affected, ensuring that miners are not hindered by perceived or actual barriers to this secondary intervention.

[Research advances in imaging techniques related to the diagnosis of pneumoconiosis].

Occupational pneumoconiosis (hereinafter referred to as pneumoconiosis) is the most harmful occupational disease in China. According to the current standard GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis, pneumoconiosis is mainly diagnosed and staged by high kilovolt or digital radiography. Chest radiography in pneumoconiosis is the most widely studied and mature imaging technique in the diagnosis of pneumoconiosis. However, this technique has some limitations in the screening of some early pneumoconiosis and occupational health examination, and there is a certain risk of missed diagnosis and misdiagnosis. With the continuous development of imaging examination technology, computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography and artificial intelligence technology as auxiliary imaging examination methods have shown different diagnostic values in the research of auxiliary diagnosis of pneumoconiosis. This paper summarizes the advantages and problems in the application of various kinds of imaging techniques, which provides a direction for the future research of imaging techniques related to the diagnosis of pneumoconiosis.

Fluoro-edenite non-neoplastic diseases in Biancavilla (Sicily, Italy): pleural plaques and/or pneumoconiosis?

BACKGROUND: A mesothelioma cluster in Biancavilla (Sicily, Italy), drew attention to fluoro-edenite, a fibre classified by International Agency for Research on Cancer as carcinogenic to humans. Significant excesses in mortality and morbidity were observed for respiratory diseases and a significant excess of pneumoconiosis hospitalizations was reported. OBJECTIVE: Aim of this study is to assess the characters of the lung damage in Biancavilla residents hospitalized with pneumoconiosis or asbestosis diagnoses. METHODOLOGY: Medical records, available radiographs and computed tomography scans were collected. The obtained imaging was reviewed by a panel of three specialists and focused on pleural and parenchymal abnormalities. Cases with an ILO-BIT or ICOERD score equal or greater than 2 were considered positive for a pneumoconiosis-like damage, cases with a score lower than 2 or insufficient quality of imaging were considered inconclusive. If no pneumoconiotic aspects were present the cases were classified as negative. RESULTS: Out of 38 cases, diagnostic imaging for 25 cases were found. Ten cases out of 25 showed asbestosis-like features, nine subjects were considered negative. In six patients' results were inconclusive. CONCLUSIONS: Asbestosis-like features were substantiated in Biancavilla residents without known occupational exposure to asbestos. Further studies to estimate population respiratory health are required. Experimental studies on the fibrogenic potential of fluoro-edenite are needed.

Tetrandrine slows the radiographic progression of progressive massive fibrosis in pneumoconiosis: a retrospective cohort study.

OBJECTIVE: This study aims to explore the clinical effect of Tetrandrine (Tet) on progressive massive fibrosis (PMF) of pneumoconiosis. METHODS: This retrospective study collected 344 pneumoconiosis patients with PMF, and 127 were eligible for the final analysis, including 57 patients in the Tet group and 70 patients in the control group. The progress of imaging and lung function were compared between the two groups. RESULTS: After 13 months (median) of treatment, the size of PMF was smaller in the Tet group than that in the control group (1526 vs. 2306, p=0.001), and the size was stable in the Tet group (1568 vs. 1526, p= 0.381), while progressed significantly in the control group (2055 vs. 2306, p=0.000). The small nodule profusion and emphysema were also milder than that in the control group (6.0 vs. 7.5, p=0.046 and 8.0 vs. 12, p=0.016 respectively). Pulmonary ventilation function parameters FVC and FEV1 improved in the Tet group (3222 vs. 3301, p=0.021; 2202 vs. 2259, p=0.025 respectively) and decreased in the control group (3272 vs. 3185, p= 0.00; 2094 vs. 1981, p=0.00 respectively). FEV1/FVC was also significantly higher in the Tet group than that in the control group (68.45vs. 60.74, p=0.001). However, similar result was failed to observed for DLco%, which showed a significant decrease in both groups. CONCLUSION: Tet has shown great potential in the treatment of PMF by slowing the progression of pulmonary fibrosis and the decline of lung function.

[Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage].

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.

Artificial Intelligence in Quantitative Chest Imaging Analysis for Occupational Lung Disease.

Occupational lung disease manifests complex radiologic findings which have long been a challenge for computer-assisted diagnosis (CAD). This journey started in the 1970s when texture analysis was developed and applied to diffuse lung disease. Pneumoconiosis appears on radiography as a combination of small opacities, large opacities, and pleural shadows. The International Labor Organization International Classification of Radiograph of Pneumoconioses has been the main tool used to describe pneumoconioses and is an ideal system that can be adapted for CAD using artificial intelligence (AI). AI includes machine learning which utilizes deep learning or an artificial neural network. This in turn includes a convolutional neural network. The tasks of CAD are systematically described as classification, detection, and segmentation of the target lesions. Alex-net, VGG16, and U-Net are among the most common algorithms used in the development of systems for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the pursuit of CAD of pneumoconioses including our recent proposal of a new expert system.

Imaging diagnosis of pneumoconiosis with predominant nodular pattern: HRCT and pathologic findings.

The radiological patterns of known pneumoconiosis have been changing in recent years. The basic pathology in pneumoconiosis is the presence of dust macules, mixed dust fibrosis, nodules, diffuse interstitial fibrosis, and progressive massive fibrosis. These pathologic changes can coexist in dust-exposed workers. High resolution CT reflects pathological findings in pneumoconiosis and is useful for the diagnosis. Pneumoconiosis such as silicosis, coal workers' pneumoconiosis, graphite pneumoconiosis, and welder's pneumoconiosis, has predominant nodular HRCT pattern. Diffuse interstitial pulmonary fibrosis is sometimes found in the lungs of this pneumoconiosis. In the early stages of metal lung, such as aluminosis and hard metal lung, centrilobular nodules are predominant findings, and in the advanced stages, reticular opacities are predominant findings. The clinician must understand the spectrum of expected imaging patterns related to known dust exposures and novel exposures. In this article, HRCT and pathologic findings of pneumoconiosis with predominant nodular opacities are shown.

Serum Bilirubin Levels and Disease Severity in Patients with Pneumoconiosis.

Aim: To investigate the association between serum bilirubin and disease severity in patients with pneumoconiosis. Methods: The study comprised 45 patients with pneumoconiosis retrospectively; all pneumoconiosis patients were classified into I, II, and III stage according to the radiological severity. Results: Serum direct bilirubin levels were significantly lower in III stage pneumoconiosis patients than those in I/II stage (p = 0.012) but not serum indirect bilirubin. Serum direct bilirubin was negatively correlated with radiological severity in patients with pneumoconiosis (r = -0.320; p = 0.032); by multiple linear-regression analysis, we observed that serum direct bilirubin levels had independent association with radiological severity in patients with pneumoconiosis (beta = -0.459; p = 0.005). Conclusions: Serum direct bilirubin levels are negatively associated with disease severity in patients with pneumoconiosis.

Perceptual and objective physical quality of chest images: a comparison between digital radiographic chest images processed for cancer screening and pneumoconiosis screening in Japan.

This study (1) evaluated the perceptual and objective physical quality of digital radiographic chest images processed for different purposes (routine hospital use, lung cancer screening, and pneumoconiosis screening), and (2) quantified objectively the quality of chest images visually graded by the Japan National Federation of Industrial Health Organization (ZENEIREN). Four observers rated the images using a visual grading score (VGS) according to ZENEIREN's quality criteria. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured. Between groups, differences were assessed using ANOVA (followed by Bonferroni multiple comparisons) or unpaired t-test. The Pearson's correlation coefficients were calculated for the correlation between perceptual quality and objective physical image quality. The image quality perceived by the observers and the SNR measurements were highest for the images generated using parameters recommended for lung cancer screening. The images processed for pneumoconiosis screening were rated poorest by the observers and showed the lowest objective physical quality measurements. The chest images rated high quality by ZENEIREN generally showed a higher objective physical image quality. The SNR correlated well with VGS, but CNR did not. Highly significant differences between the processing parameters indicate that image processing strongly influences the perceptual quality of digital radiographic chest images.

[Study on optimization of image processing parameters of pneumoconiosis by DR].

Objective: To explore the effect of different post-processing parameters of digital radiography (DR) on the quality of chest X-ray for pneumoconiosis diagnosis, and to provide suggestions on parameter setting suitable for this kind of DR machine. Methods: From January 1, 2022 to June 30, 2022, the chest films of 35 workers in the department of radiology of Hangzhou occupational disease prevention and treatment hospital were randomly selected and printed after setting different image post-processing parameters. The quality of chest film was evaluated by the measurement of optical densitometer and the combination of subjective and objective by professional physicians. Results: When the density is set to 2 and the contrast/detail contrast is 4.5, the optical density of each area of DR chest film meets the requirements of chest X-ray quality, and the qualified rate of physician quality evaluation is the highest. Conclusion: Reasonable setting of image post-processing parameters can improve the quality of chest radiograph.

[Application of artificial intelligence in digital chest radiography diagnosis of pneumoconiosis].

Pneumoconiosis is the occupational disease with the highest burden in China currently. The diagnosis of pneumoconiosis mainly relies on manual reading of X-ray high-kilovoltage or digital photography chest radiograph, which has some problems such as low efficiency, strong subjectivity, and cannot accurately judge the critical lesions. With the progress of machine-aided diagnosis technology, the efficient, objective and quantitative of artificial intelligence diagnosis technology just solve the shortcomings above. This paper reviews the research progress in digital chest radiography diagnosis of pneumoconiosis using artificial intelligence technology, especially deep learning model, combined with the limitations of conventional manual reading, in order to clarify the application prospect of artificial intelligence technology in the diagnosis of pneumoconiosis by digital chest radiography, and provide a direction for future research in this field.

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.

Tracheobronchial variations in Pneumoconiosis cases: multidetector computed tomography diagnosis.

Background: Tracheobronchial variations (TBVs) are more common than previously believed due to the increasing use of multi-detector computed tomography (MDCT). This study aimed to assess TBVs in cases of pneumoconiosis, one of the oldest occupational diseases that still poses a threat to public health. Methods: This was a descriptive study that involved reviewing chest MDCT images of 34 cases of pneumoconiosis and 34 control cases retrospectively from January 2020 to April 2022. Variations in the trachea, right main bronchus, left main bronchus, lobar and segmental branches of the cases in the patient and control groups were evaluated according to Boyden's nomenclature. Results: The frequency of TBV was 32.4% in pneumoconiosis cases. Although the frequency of TBV was higher in the patient group than in the control group, the difference was not statistically significant (p=0.086). Furthermore, there was no significant difference in terms of TBV classification between the patient and control groups (p=0.407). Additionally, the presence of TBV did not affect the distribution of International Labour Organization categories in pneumoconiosis cases (p=0.360). Conclusions: Although our study provides initial insights into the occurrence of TBVs in pneumoconiosis cases, further research is needed to clarify the relationship between these variations and the disease.