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.

Patrón de lectura radiológica digital para la clasificación de imágenes compatibles con silicosis / Digital reading radiation pattern for classification of image about silicosis (P0140-2013)

La silicosis es una enfermedad sin tratamiento. El programa de vigilancia de trabajadores expuestos a sílice está diseñado para detectarla lo más precozmente posible mediante una radiografía de tórax. A pesar de su rol fundamental, esta adolece de una gran variabilidad técnica y de interpretación. Para disminuir estos fenómenos, la OIT creó un sistema de clasificación ampliamente usado en Chile y el mundo. Este se basó en la radiografía análoga, la que ha sido reemplazada por el mismo set análogo, digitalizado. Si bien la OIT permite el uso de este nuevo tipo de imagen, indica que el patrón de comparación debe seguir siendo el análogo. Muchos estudios han demostrado que es mejor utilizar patrones de comparación digitales. El objetivo de este trabajo fue generar un set de radiografías digitales que representen las profusiones centrales de opacidades redondas del sistema de clasificación OIT. De un universo de 32.826 radiografías de pacientes expuestos a sílice, se obtuvo 252 radiografías, de las cuales 54 fueron informadas por 3 lectores acreditados, seleccionándose 8 casos que contaban con opacidades redondas puras de diferentes profusiones, las que se proponen como patrón digital para Chile.

Silicosis is a disease without treatment. The surveillance program for workers exposed to silica is designed to detect as early as possible by a chest radiograph. Despite its fundamental role, it suffers from a great technique and interpretation variability. To reduce these phenomena, the ILO created a classification system widely used in Chile and the world. This was based on analog radiography, which has been replaced by the same analog set, digitized. While the ILO permits the use of this new type of image it indicates that the pattern comparison must remain analog. Many studies have shown that it is best to use digital comparison patterns. The objective of this work was to generate a set of digital x-rays representing the central profusion of round opacities of ILO classification system. From a universe of 32,826 x-rays of patients exposed to silica, 252 films, of which 54 were reported for 3 accredited readers, selected 8 cases that had pure round opacities of different profusions was obtained, which are proposed as digital pattern for Chile.

Prevalence of pneumoconiosis among coal miners of Cherat, district Nowshera - Pakistan

The objective of this study was to assess the prevalence of pneumoconiosis among coal miners. This cross-sectional study was conducted among 400 coalminers of Cherat, District Nowshera, KPK, Pakistan, from July 2012 to June2013. Coal miners who worked for more than six months were included. Medical camps were arranged in study areas in which Pulmonary Function Tests and CXR was conducted to estimate the prevalence of pneumoconiosis. The demographic variables were also noted on a semi structured proforma. The medical examinations of coal miner's revealed that approximately71% [n=284] of coal miners have sign and symptoms of occupational respiratory health problems. The coal miners showed an increased prevalence of coal workers pneumoconiosis [49.50%] i.e. about 47% [n=188] on Chest X-rays [P/A View] and 52.50% [n=210] on Pulmonary Function Tests has findings of coal workers pneumoconiosis. Only 31.75% [n=127] of coal miners have normal pulmonary function tests and 35% [n=140] have normal chest x-ray findings during medical assessment. The prevalence of the pneumoconiosis was high [49.50%] among the Cherat coal miners; and needs proper consideration and attention from the health sector and mining department in order to reduce the high burden of pneumoconiosis among coal miners

Development of Standard Digital Images for Pneumoconiosis

We developed the standard digital images (SDIs) to be used in the classification and recognition of pneumoconiosis. From July 3, 2006 through August 31, 2007, 531 retired male workers exposed to inorganic dust were examined by digital (DR) and analog radiography (AR) on the same day, after being approved by our institutional review board and obtaining informed consent from all participants. All images were twice classified according to the International Labour Office (ILO) 2000 guidelines with reference to ILO standard analog radiographs (SARs) by four chest radiologists. After consensus reading on 349 digital images matched with the first selected analog images, 120 digital images were selected as the SDIs that considered the distribution of pneumoconiosis findings. Images with profusion category 0/1, 1, 2, and 3 were 12, 50, 40, and 15, respectively, and a large opacity were in 43 images (A = 20, B = 22, C = 1). Among pleural abnormality, costophrenic angle obliteration, pleural plaque and thickening were in 11 (9.2%), 31 (25.8%), and 9 (7.5%) images, respectively. Twenty-one of 29 symbols were present except cp, ef, ho, id, me, pa, ra, and rp. A set of 120 SDIs had more various pneumoconiosis findings than ILO SARs that were developed from adequate methods. It can be used as digital reference images for the recognition and classification of pneumoconiosis.