Does periodic lung screening of films meets standards?

Binay, Songül; Arbak, Peri; Şafak, Alp Alper; Balbay, Ege Güleç; Bilgin, Cahit; Karatas, Naciye

doi:10.12669/pjms.326.11267

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…[ 11 , 12 ] However, the diagnosis of pneumoconiosis remains challenging, is subjective, and varies among reviewers. [ 13 , 14 ] To improve the diagnostic efficiency and accuracy among radiologists, researchers have developed computer-aided diagnosis (CAD) schemes for detecting pneumoconiosis using chest radiographs as a second opinion.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Zheng,

Zeng

et al. 2024

Medicine

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang,

Zheng,

Zeng

et al. 2024

Medicine

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“…While classifying pneumoconiosis, we followed the ILO classi cation guidelines; nonetheless, we used standard radiographs collected and de ned by the Chinese Center for Disease Control and Prevention [11][12]. However, the diagnosis of pneumoconiosis remains challenging, is subjective, and varies among reviewers [13][14]. To improve the diagnostic e ciency and accuracy among radiologists, researchers have developed computer-aided diagnosis (CAD) schemes for detecting pneumoconiosis using chest radiographs as a second opinion.…”

Section: Introductionmentioning

confidence: 99%

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis

Zhang

Zheng

et al. 2023

Preprint

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Background To improve the accuracy of pneumoconiosis diagnosis, a computer-assisted method was developed. Methods Three CNNs (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1,250 chest X-ray images. Three double-blinded experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III. The results of the three physicians in agreement were considered the relative gold standards. Subsequently, three CNNs 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. Results ResNet101 was the optimal model among the three CNNs. 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. Conclusion 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.

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“…This process is laborious and subject to considerable inter- and intra-observer variations. For example, the concordance in pneumoconiosis diagnosis is between 85 and 90% among expert radiologists 11 , and around 80% in general medical staff 12 in the United States. The consistency is likely to be even worse for the screening programs conducted at community sites, especially in developing countries.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based model for screening and staging pneumoconiosis

Zhang

Rong

et al. 2021

Sci Rep

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This study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated using an independent test cohort. Every image in the training and test datasets were labeled by experienced radiologists in a double-blinded fashion. The computational model started by segmenting the lung field into six subregions. Then, convolutional neural network classification model was used to predict the opacity level for each subregion respectively. Finally, the diagnosis for each subject (normal, stage I, II, or III pneumoconiosis) was determined by summarizing the subregion-based prediction results. For the independent test cohort, pneumoconiosis screening accuracy was 0.973, with both sensitivity and specificity greater than 0.97. The accuracy for pneumoconiosis staging was 0.927, better than that achieved by two groups of radiologists (0.87 and 0.84, respectively). This study develops a deep learning-based model for screening and staging of pneumoconiosis using man-annotated chest radiographs. The model outperformed two groups of radiologists in the accuracy of pneumoconiosis staging. This pioneer work demonstrates the feasibility and efficiency of AI-assisted radiography screening and diagnosis in occupational lung diseases.

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Does periodic lung screening of films meets standards?

Cited by 10 publications

References 13 publications

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

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

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis

A deep learning-based model for screening and staging pneumoconiosis

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