Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade
<b>Background:</b> Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. <b>Methods:</b> This study aims to automate the assessment of KOA severity by training deep le...
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MDPI AG
2025-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/10/1220 |
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| author | Joo Chan Choi Min Young Jeong Young Jae Kim Kwang Gi Kim |
| author_facet | Joo Chan Choi Min Young Jeong Young Jae Kim Kwang Gi Kim |
| author_sort | Joo Chan Choi |
| collection | DOAJ |
| description | <b>Background:</b> Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. <b>Methods:</b> This study aims to automate the assessment of KOA severity by training deep learning models using the Kellgren–Lawrence grading system (class 0~4). A total of 15,000 images were used, with 3000 images collected for each grade. The learning models utilized were DenseNet201, ResNet101, and EfficientNetV2, and their performance in lesion classification was evaluated and compared. Statistical metrics, including accuracy, precision, recall, and F1-score, were employed to assess the feasibility of applying deep learning models for KOA classification. <b>Results:</b> Among these four metrics, DenseNet201 achieved the highest performance, while the ResNet101 model recorded the lowest. DenseNet201 demonstrated the best performance with an overall accuracy of 73%. The model’s accuracy by K-L grade was 80.7% for K-L Grade 0, 53.7% for K-L Grade 1, 72.7% for K-L Grade 2, 75.3% for K-L Grade 3, and 82.7% for K-L Grade 4. The model achieved a precision of 73.2%, a recall of 73%, and an F1-score of 72.7%. <b>Conclusions:</b> These results highlight the potential of deep learning models for assisting specialists in diagnosing the severity of KOA by automatically assigning K-L grades to patient data. |
| format | Article |
| id | doaj-art-230366fb6db142efb3f620e5a4d6afbb |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-230366fb6db142efb3f620e5a4d6afbb2025-08-20T01:56:16ZengMDPI AGDiagnostics2075-44182025-05-011510122010.3390/diagnostics15101220Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L GradeJoo Chan Choi0Min Young Jeong1Young Jae Kim2Kwang Gi Kim3Department of Biomedical Engineering, College of Health & Science, Gachon University, Seongnam-si 461701, Republic of KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaGachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon 21565, Republic of KoreaDepartment of Biomedical Engineering, College of Health & Science, Gachon University, Seongnam-si 461701, Republic of Korea<b>Background:</b> Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. <b>Methods:</b> This study aims to automate the assessment of KOA severity by training deep learning models using the Kellgren–Lawrence grading system (class 0~4). A total of 15,000 images were used, with 3000 images collected for each grade. The learning models utilized were DenseNet201, ResNet101, and EfficientNetV2, and their performance in lesion classification was evaluated and compared. Statistical metrics, including accuracy, precision, recall, and F1-score, were employed to assess the feasibility of applying deep learning models for KOA classification. <b>Results:</b> Among these four metrics, DenseNet201 achieved the highest performance, while the ResNet101 model recorded the lowest. DenseNet201 demonstrated the best performance with an overall accuracy of 73%. The model’s accuracy by K-L grade was 80.7% for K-L Grade 0, 53.7% for K-L Grade 1, 72.7% for K-L Grade 2, 75.3% for K-L Grade 3, and 82.7% for K-L Grade 4. The model achieved a precision of 73.2%, a recall of 73%, and an F1-score of 72.7%. <b>Conclusions:</b> These results highlight the potential of deep learning models for assisting specialists in diagnosing the severity of KOA by automatically assigning K-L grades to patient data.https://www.mdpi.com/2075-4418/15/10/1220knee osteoarthritisKellgren–Lawrence grading systemdeep learningDenseNet201ResNet101EfficientNetV2 |
| spellingShingle | Joo Chan Choi Min Young Jeong Young Jae Kim Kwang Gi Kim Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade Diagnostics knee osteoarthritis Kellgren–Lawrence grading system deep learning DenseNet201 ResNet101 EfficientNetV2 |
| title | Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade |
| title_full | Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade |
| title_fullStr | Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade |
| title_full_unstemmed | Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade |
| title_short | Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade |
| title_sort | artificial intelligence model assists knee osteoarthritis diagnosis via determination of k l grade |
| topic | knee osteoarthritis Kellgren–Lawrence grading system deep learning DenseNet201 ResNet101 EfficientNetV2 |
| url | https://www.mdpi.com/2075-4418/15/10/1220 |
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