Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis
<b>Background:</b> Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy o...
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MDPI AG
2024-11-01
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| author | Kyu-Hong Lee Ro-Woon Lee Jae-Sung Yun Myung-Sub Kim Hyun-Seok Choi |
| author_facet | Kyu-Hong Lee Ro-Woon Lee Jae-Sung Yun Myung-Sub Kim Hyun-Seok Choi |
| author_sort | Kyu-Hong Lee |
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| description | <b>Background:</b> Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. <b>Methods:</b> We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. <b>Results:</b> Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. <b>Conclusions:</b> This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows. |
| format | Article |
| id | doaj-art-eaf048cbc6454bd68869be3745c1fea2 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-eaf048cbc6454bd68869be3745c1fea22025-08-20T02:14:22ZengMDPI AGDiagnostics2075-44182024-11-011421245110.3390/diagnostics14212451Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image AnalysisKyu-Hong Lee0Ro-Woon Lee1Jae-Sung Yun2Myung-Sub Kim3Hyun-Seok Choi4Department of Radiology, Inha University College of Medicine, 27 Inhang-ro, Jung-gu, Incheon 22332, Republic of KoreaDepartment of Radiology, Inha University College of Medicine, 27 Inhang-ro, Jung-gu, Incheon 22332, Republic of KoreaDepartment of Radiology, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of KoreaDeepnoid Inc., Seoul 08376, Republic of Korea<b>Background:</b> Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. <b>Methods:</b> We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. <b>Results:</b> Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. <b>Conclusions:</b> This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.https://www.mdpi.com/2075-4418/14/21/2451no-codingosteoarthritisartificial intelligence |
| spellingShingle | Kyu-Hong Lee Ro-Woon Lee Jae-Sung Yun Myung-Sub Kim Hyun-Seok Choi Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis Diagnostics no-coding osteoarthritis artificial intelligence |
| title | Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis |
| title_full | Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis |
| title_fullStr | Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis |
| title_full_unstemmed | Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis |
| title_short | Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis |
| title_sort | automated diagnosis of knee osteoarthritis using resnet101 on a deep phi leveraging a no code ai platform for efficient and accurate medical image analysis |
| topic | no-coding osteoarthritis artificial intelligence |
| url | https://www.mdpi.com/2075-4418/14/21/2451 |
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