Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images
Abstract The Kellgren–Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician’s subjective assessment. M...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50210-4 |
| _version_ | 1797377052289007616 |
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| author | Sun-Woo Pi Byoung-Dai Lee Mu Sook Lee Hae Jeong Lee |
| author_facet | Sun-Woo Pi Byoung-Dai Lee Mu Sook Lee Hae Jeong Lee |
| author_sort | Sun-Woo Pi |
| collection | DOAJ |
| description | Abstract The Kellgren–Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician’s subjective assessment. Moreover, the accuracy varies significantly depending on the clinician’s experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis. |
| first_indexed | 2024-03-08T19:47:27Z |
| format | Article |
| id | doaj.art-7af50336e04e43df84381ff121ad2941 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:27Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7af50336e04e43df84381ff121ad29412023-12-24T12:16:03ZengNature PortfolioScientific Reports2045-23222023-12-0113111710.1038/s41598-023-50210-4Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray imagesSun-Woo Pi0Byoung-Dai Lee1Mu Sook Lee2Hae Jeong Lee3Division of AI and Computer Engineering, Kyonggi UniversityDivision of AI and Computer Engineering, Kyonggi UniversityDepartment of Radiology, Keimyung University Dongsan HospitalDepartment of Pediatrics, Samsung Changwon Hospital, Sungkyunkwan University School of MedicineAbstract The Kellgren–Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician’s subjective assessment. Moreover, the accuracy varies significantly depending on the clinician’s experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis.https://doi.org/10.1038/s41598-023-50210-4 |
| spellingShingle | Sun-Woo Pi Byoung-Dai Lee Mu Sook Lee Hae Jeong Lee Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images Scientific Reports |
| title | Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images |
| title_full | Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images |
| title_fullStr | Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images |
| title_full_unstemmed | Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images |
| title_short | Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images |
| title_sort | ensemble deep learning networks for automated osteoarthritis grading in knee x ray images |
| url | https://doi.org/10.1038/s41598-023-50210-4 |
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