Abstract

Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee joint. Currently, OA diagnosis relies on the examination of plain radiographs, a method susceptible to subjectivity and time consumption. This study aims to automatically assess the severity of knee OA using the "Kellgren and Lawrence (KL) grading system" based on plain X-rays. Leveraging 1650 digitized knee X-ray images, we implemented a custom MobileNetV2 architecture for a convolutional neural network with four distinct orientations. The methodology comprises two models: a fixed base and a trainable head. The MobileNetV2 network serves as the base model, while the proposed head model architecture includes an average pooling layer followed by a fully connected layer to enhance network efficiency. Results indicate that, except for grades 1 and 2, the methodology correctly identified KL grades with an accuracy of over 90%. Overall, the proposed approach demonstrates promising potential for classifying knee OA based on plain X-rays, achieving a 95% accuracy in detecting severe knee OA (stage 4). Researchers acknowledge the superior performance of their methodology compared to previous models in similar investigations, suggesting its effectiveness in forecasting OA severity based on radiographic imaging. Furthermore, the study's results support the effectiveness of deep learning-based approaches in diagnosing OA severity, with significant implications for improving patient outcomes. Although the suggested methodology shows a maximum accuracy rate of 95% in identifying severe knee OA cases (specifically, stage 4), there is a need for enhancements to address the misdiagnosis issue in stage 1 and 2 knee OA, where the accuracy rate is at 87%. This misdiagnosis arises from the similarity in characteristics between these stages.

Keyword

Knee OA, Detection disease, Deep learning, Image classification, Pre-trained networks.

Cite this article

Ghazwan A, Qazzaz SA, Abdulmunem AA

Refference

[1][1]Oliveria SA, Felson DT, Reed JI, Cirillo PA, Walker AM. Incidence of symptomatic hand, hip, and knee osteoarthritis among patients in a health maintenance organization. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology. 1995; 38(8):1134-41.

[2][2]Kawano MM, Araújo IL, Castro MC, Matos MA. Assessment of quality of life in patients with knee osteoarthritis. Acta Ortopedica Brasileira. 2015; 23:307-10.

[3][3]Kellgren JH, Lawrence J. Radiological assessment of osteo-arthrosis. Annals of the Rheumatic Diseases. 1957; 16(4):494-502.

[4][4]Mahum R, Rehman SU, Meraj T, Rauf HT, Irtaza A, El-sherbeeny AM, et al. A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors. 2021; 21(18):1-18.

[5][5]Jamshidi A, Pelletier JP, Labbe A, Abram F, Martel‐pelletier J, Droit A. Machine learning–based individualized survival prediction model for total knee replacement in osteoarthritis: data from the osteoarthritis initiative. Arthritis Care & Research. 2021; 73(10):1518-27.

[6][6]Kuran EC, Kuran U, Er MB. Sub-image histogram equalization using coot optimization algorithm for segmentation and parameter selection. CS & IT conference proceedings. 2022(pp. 33-46).

[7][7]Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, et al. Discovering knee osteoarthritis imaging features for diagnosis and prognosis: review of manual imaging grading and machine learning approaches. Journal of Healthcare Engineering. 2022; 2022:1-20.

[8][8]Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annual Review of Biomedical Engineering. 2017; 19:221-48.

[9][9]Gan HS, Ramlee MH, Wahab AA, Lee YS, Shimizu A. From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research. Artificial Intelligence Review. 2021; 54(4):2445-94.

[10][10]Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Scientific Reports. 2018; 8(1):1-10.

[11][11]Antony J, Mcguinness K, Oconnor NE, Moran K. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In 23rd international conference on pattern recognition 2016 (pp. 1195-200). IEEE.

[12][12]Antony J, Mcguinness K, Moran K, O’connor NE. Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks. In machine learning and data mining in pattern recognition: 13th international conference, New York, USA, 2017 (pp. 376-90). Springer International Publishing.

[13][13]Yeoh PS, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, et al. Emergence of deep learning in knee osteoarthritis diagnosis. Computational Intelligence and Neuroscience. 2021; 2021:1-20.

[14][14]Xue Y, Zhang R, Deng Y, Chen K, Jiang T. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PloS one. 2017; 12(6):1-9.

[15][15]Schwartz AJ, Clarke HD, Spangehl MJ, Bingham JS, Etzioni DA, Neville MR. Can a convolutional neural network classify knee osteoarthritis on plain radiographs as accurately as fellowship-trained knee arthroplasty surgeons?. The Journal of Arthroplasty. 2020; 35(9):2423-8.

[16][16]Chen P, Gao L, Shi X, Allen K, Yang L. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics. 2019; 75:84-92.

[17][17]Gornale SS, Patravali PU, Hiremath PS. Automatic detection and classification of knee osteoarthritis using hus invariant moments. Frontiers in Robotics and AI. 2020; 7:591827.

[18][18]Guan B, Liu F, Haj-mirzaian A, Demehri S, Samsonov A, Neogi T, et al. Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period. Osteoarthritis and Cartilage. 2020; 28(4):428-37.

[19][19]Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative. Radiology. 2020; 296(3):584-93.

[20][20]Schiratti JB, Dubois R, Herent P, Cahané D, Dachary J, Clozel T, et al. A deep learning method for predicting knee osteoarthritis radiographic progression from MRI. Arthritis Research & Therapy. 2021; 23:1-10.

[21][21]Kondal S, Kulkarni V, Gaikwad A, Kharat A, Pant A. Automatic grading of knee osteoarthritis on the Kellgren-Lawrence scale from radiographs using convolutional neural networks. In advances in deep learning, artificial intelligence and robotics: proceedings of the 2nd international conference on deep learning, artificial intelligence and robotics, 2022 (pp. 163-173). Cham: Springer International Publishing.

[22][22]Jain RK, Sharma PK, Gaj S, Sur A, Ghosh P. Knee osteoarthritis severity prediction using an attentive multi-scale deep convolutional neural network. Multimedia Tools and Applications. 2023:1-8.

[23][23]Guan B, Liu F, Mizaian AH, Demhri S, Neogi T, Guermazi A, et al. Deep learning approach to predict radiographic knee osteoarthritis progression. Osteoarthritis and Cartilage. 2019; 27:S395-6.

[24][24]Wang Y, Wang X, Gao T, Du L, Liu W. An automatic knee osteoarthritis diagnosis method based on deep learning: data from the osteoarthritis initiative. Journal of Healthcare Engineering. 2021; 2021:1-10.

[25][25]Ahmed SM, Mstafa RJ. Identifying severity grading of knee osteoarthritis from X-ray images using an efficient mixture of deep learning and machine learning models. Diagnostics. 2022; 12(12):1-25.

[26][26]Khalid A, Senan EM, Al-wagih K, Ali AMM, Alkhraisha ZM. Hybrid techniques of X-ray analysis to predict knee osteoarthritis grades based on fusion features of CNN and handcrafted. Diagnostics. 2023; 13(9):1-26.

[27][27]Abd ES, Elmogy M, Abd EAA. A fully automatic fine tuned deep learning model for knee osteoarthritis detection and progression analysis. Egyptian Informatics Journal. 2023; 24(2):229-40.

[28][28]Kokkotis C, Ntakolia C, Moustakidis S, Giakas G, Tsaopoulos D. Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Physical and Engineering Sciences in Medicine. 2022; 45(1):219-29.

[29][29]Mccabe PG, Lisboa P, Baltzopoulos B, Olier I. Externally validated models for first diagnosis and risk of progression of knee osteoarthritis. PloS one. 2022; 17(7):1-23.

[30][30]Guan B, Liu F, Mizaian AH, Demehri S, Samsonov A, Guermazi A, Kijowski R. Deep learning approach to predict pain progression in knee osteoarthritis. Skeletal Radiology. 2022:1-11.

[31][31]Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. Journal of Magnetic Resonance Imaging. 2019; 49(2):400-10.

[32][32]Landsmeer ML, Runhaar J, Van MM, Oei EH, Schiphof D, Bindels PJ, et al. Predicting knee pain and knee osteoarthritis among overweight women. The Journal of the American Board of Family Medicine. 2019; 32(4):575-84.

[33][33]Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthritis and Cartilage. 2018; 26(12):1643-50.

[34][34]Widera P, Welsing PM, Ladel C, Loughlin J, Lafeber FP, Petit DF, et al. Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Scientific Reports. 2020; 10(1):1-15.

[35][35]Huang C, Xu Z, Shen Z, Luo T, Li T, Nissman D, et al. DADP: dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the osteoarthritis initiative. Medical Image Analysis. 2022; 77:102343.

[36][36]Tolpadi AA, Lee JJ, Pedoia V, Majumdar S. Deep learning predicts total knee replacement from magnetic resonance images. Scientific Reports. 2020; 10(1):1-12.

[37][37]Li MD, Chang K, Bearce B, Chang CY, Huang AJ, Campbell JP, et al. Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. NPJ Digital Medicine. 2020; 3(1):1-9.

[38][38]Swiecicki A, Li N, Odonnell J, Said N, Yang J, Mather RC, et al. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Computers in Biology and Medicine. 2021; 133:104334.

[39][39]Saini D, Khosla A, Chand T, Chouhan DK, Prakash M. Automated knee osteoarthritis severity classification using three‐stage preprocessing method and VGG16 architecture. International Journal of Imaging Systems and Technology. 2023; 33(3):1028-47.

[40][40]Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. Journal of Digital Imaging. 2019; 32(3):471-7.

[41][41]Liu B, Luo J, Huang H. Toward automatic quantification of knee osteoarthritis severity using improved faster R-CNN. International Journal of Computer Assisted Radiology and Surgery. 2020; 15:457-66.

[42][42]https://data.mendeley.com/datasets/t9ndx37v5h/1. Accessed 30 October 2023.

[43][43]Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: inverted residuals and linear bottlenecks. In proceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 4510-20).

[44][44]Thomas KA, Kidziński Ł, Halilaj E, Fleming SL, Venkataraman GR, Oei EH, et al. Automated classification of radiographic knee osteoarthritis severity using deep neural networks. Radiology: Artificial Intelligence. 2020; 2(2):1-10.

[45][45]Yong CW, Teo K, Murphy BP, Hum YC, Tee YK, Xia K, et al. Knee osteoarthritis severity classification with ordinal regression module. Multimedia Tools and Applications. 2021:1-3.

[46][46]Dharmani BC, Khatri K. Deep learning for knee osteoarthritis severity stage detection using X-Ray images. In international conference on communication systems & networks 2023 (pp. 78-83). IEEE.

[47][47]Li W, Feng J, Zhu D, Xiao Z, Liu J, Fang Y, et al. Nomogram model based on radiomics signatures and age to assist in the diagnosis of knee osteoarthritis. Experimental Gerontology. 2023; 171:112031.

[48][48]Rehman A, Raza A, Alamri FS, Alghofaily B, Saba T. Transfer learning-based smart features engineering for osteoarthritis diagnosis from knee X-ray images. IEEE Access. 2023; 11:71326-38.