Sohail Muhammad, Azad Muhammad Muzammil, Kim Heung Soo
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea.
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea.
Comput Biol Med. 2025 Mar;186:109641. doi: 10.1016/j.compbiomed.2024.109641. Epub 2024 Dec 31.
Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL) grading system is a common method that is accepted worldwide, uses five grades to classify the severity of OA, and relies on the ability of the orthopedist to accurately interpret radiograph images. To improve the accuracy of radiograph image interpretation, artificial intelligence-assisted models have been developed that include shallow or deep learning approaches and multi-step techniques; however, their accuracy remains variable. This work proposes a transfer learning approach using an InceptionV3-based model fine-tuned on the Osteoarthritis Initiative dataset, and aims to enhance the identification of OA severity levels through dual-stage preprocessing and convolutional neural networks for feature extraction. The fine-tuned IV3 (FT-IV3) model outperformed the IV3 model with training, validation, and testing accuracies of (96.33, 93.82, and 92.25) %, compared to IV3 accuracies of (91.64, 82.04, and 86.20) %, respectively. Additionally, Cohen's Kappa value for the FT-IV3 model (90.69 %) exceeds that of the IV3 model (83.15 %), indicating a better diagnosis of OA severity. This improvement allows the FT-IV3 model to effectively classify moderate and severe-grade OA.
骨关节炎（OA）是一种导致身体功能受限的常见病症。早期发现OA对于有效控制该病症至关重要。然而，早期关节炎的诊断仍然具有挑战性。凯尔格伦和劳伦斯（KL）分级系统是一种在全球范围内被广泛接受的常用方法，它使用五个等级来对OA的严重程度进行分类，并且依赖于骨科医生准确解读X光片图像的能力。为了提高X光片图像解读的准确性，已经开发了包括浅层或深度学习方法以及多步骤技术的人工智能辅助模型；然而，它们的准确性仍然参差不齐。这项工作提出了一种迁移学习方法，该方法使用基于InceptionV3的模型在骨关节炎倡议数据集上进行微调，旨在通过双阶段预处理和卷积神经网络进行特征提取来增强对OA严重程度的识别。经过微调的IV3（FT-IV3）模型在训练、验证和测试准确率分别为（96.33%、93.82%和92.25%）时，优于IV3模型，而IV3模型的准确率分别为（91.64%、82.04%和86.20%）。此外，FT-IV3模型的科恩卡帕值（90.69%）超过了IV3模型的科恩卡帕值（83.15%），表明对OA严重程度的诊断更好。这种改进使得FT-IV3模型能够有效地对中度和重度OA进行分类。
