Pneumoconiosis is the most widely distributed and harmful occupational disease in China [1, 2]. It is characterized by the diffuse fibrosis of lung tissue, caused by long-term inhalation of inorganic mineral dust and retention in the lungs during occupational activities. The cumulative number of cases has reached one million in china and it continues to increase at a rate of more than 20,000 new cases per year [2]. Dust inhaled through the respiratory tract can include silicon dioxide molecules, which cause pneumoconiosis alveolitis, focal lesions, nodular lesions, dusty fibrosis, massive fibrosis, and other pathological changes in the lungs. These lesions primarily manifest as small round structures with an irregular opacity, diffuse interstitial fibrosis of the lungs, and silicosis masses.
Conventional classification of pneumoconiosis is primarily based on the "International X-ray Classification of Pneumoconiosis" guidelines issued by the International Labor Organization in 2011 [3, 4]. The current standard in China is the "Diagnosis of Occupational Pneumoconiosis" (GBZ 70-2015), which is based on the correct interpretation of chest X-ray films using the profusion of small opacity, lung zone distributions, and pleural plaques to diagnose and classify pneumoconiosis [5, 6].
As the occurrence and progression of pneumoconiosis is a continuous process, the profusion of small opacity lesions in chest radiographs is also a sequential procedure. While professional training, comparisons of standard film, and improvements to imaging equipment and technology can increase the diagnostic accuracy of occupational physicians, inter-clinician differences remain high due to the subjective nature of image interpretation. Prior studies under the same external conditions have suggested that inconsistent judgments based on the shape, size, and number of small opacity lesions are the primary cause of inconsistent pneumoconiosis classification [7]. As such, increasing the objectivity, accuracy, and consistency of the diagnostic process is highly important.
Computer-aided diagnosis (CAD) provides an objective methodology for improving the interpretation of medical images. The rapid development of computer technology and medical imaging equipment has enabled the effective combination of artificial intelligence and image processing in recent years, which has led to improvements in detection and evaluation of disease severity [8]. Using computer-generated results as a reference, radiologists can draw more accurate conclusions for disease screenings and cancer risk assessments [9–12].
Several recent studies have investigated the application of CAD technology to the diagnosis of pneumoconiosis. New approaches have been developed for image preprocessing, characterization extraction, classifier selection, and optimization [13–15]. However, acquiring the annotated training data used in conventional machine learning models can be both time- and cost-prohibitive, as lesions must be labeled manually by clinical experts. Subjective error and bias can also become problematic in this process.
The rapid development of artificial intelligence has led to broad applications of deep learning (DL) algorithms for medical image analysis. These models utilize a multi-layer network to facilitate the automated learning of implicit relationships within the data. The resulting characteristics are often more diverse and expressive, particularly in tumor imaging applications. DL can provide semi-supervised or unsupervised autonomous learning of a target image for classification tasks. It can also synthesize images with the same characteristics and imitate the independent learning and analysis capabilities of humans, thereby reducing the subjectivity of extracted features [16–19]. At present, there is no relevant report on the evaluation of pneumoconiosis imaging diagnoses based on deep learning computer-aided diagnostic technology.
Convolutional neural networks (CNNs) and deep residual network (DRNs), an extension of CNNs, are common DL algorithms used for image classification [20]. These models provide the advantages of simplicity, practicality, and generalizability. Alternative models have also been proposed with varying convolutional layer quantities, including ResNet18, ResNet50, and ResNet101, which includes five network depths, a total of 100 convolutional layers, and a connection layer. These and other similar algorithms have been widely applied to image segmentation, detection, and recognition tasks [21, 22]. This study focused on assessing the application value of DL-based CAD technology in diagnosing pneumoconiosis.