An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis
Rheumatoid arthritis (RA) is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manu...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/5485606 |
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| _version_ | 1849398525284057088 |
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| author | Zijian Wang Jian Liu Zongyun Gu Chuanfu Li |
| author_facet | Zijian Wang Jian Liu Zongyun Gu Chuanfu Li |
| author_sort | Zijian Wang |
| collection | DOAJ |
| description | Rheumatoid arthritis (RA) is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems and automated the overall scoring on hand X-rays. The depthwise separable (Dwise) convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior (two-handed, side by side) images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model. |
| format | Article |
| id | doaj-art-a1dd71168bdd429ca5b139ccbe563ff8 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a1dd71168bdd429ca5b139ccbe563ff82025-08-20T03:38:35ZengWileyComplexity1099-05262022-01-01202210.1155/2022/5485606An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid ArthritisZijian Wang0Jian Liu1Zongyun Gu2Chuanfu Li3College of Medical Information EngineeringFirst Clinical Medical CollegeCollege of Medical Information EngineeringCollege of Medical Information EngineeringRheumatoid arthritis (RA) is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems and automated the overall scoring on hand X-rays. The depthwise separable (Dwise) convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior (two-handed, side by side) images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model.http://dx.doi.org/10.1155/2022/5485606 |
| spellingShingle | Zijian Wang Jian Liu Zongyun Gu Chuanfu Li An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis Complexity |
| title | An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis |
| title_full | An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis |
| title_fullStr | An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis |
| title_full_unstemmed | An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis |
| title_short | An Efficient CNN for Hand X-Ray Overall Scoring of Rheumatoid Arthritis |
| title_sort | efficient cnn for hand x ray overall scoring of rheumatoid arthritis |
| url | http://dx.doi.org/10.1155/2022/5485606 |
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