{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:25:51Z","timestamp":1781839551115,"version":"3.54.5"},"reference-count":95,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004728","name":"VIT University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004728","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.bspc.2026.109771","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:26:10Z","timestamp":1771961170000},"page":"109771","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PB","title":["Deep learning in knee osteoarthritis: A task-based systematic review of recent advances"],"prefix":"10.1016","volume":"119","author":[{"given":"Lavanya","family":"R.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudha","family":"Senthilkumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"16","key":"10.1016\/j.bspc.2026.109771_b1","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.3390\/healthcare12161648","article-title":"Cartilage Integrity: A Review of Mechanical and Frictional Properties and Repair Approaches in Osteoarthritis","volume":"12","author":"Krakowski","year":"2024","journal-title":"Healthcare"},{"key":"10.1016\/j.bspc.2026.109771_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104627","article-title":"Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis.","volume":"157","author":"Bayramoglu","year":"2022","journal-title":"Int. J. Med. Informat."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b3","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-20132-7","article-title":"Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach","volume":"8","author":"Tiulpin","year":"2018","journal-title":"Sci. Rep."},{"issue":"12","key":"10.1016\/j.bspc.2026.109771_b4","doi-asserted-by":"crossref","first-page":"6896","DOI":"10.3390\/app15126896","article-title":"Articular Cartilage: Structure, Biomechanics, and the Potential of Conventional and Advanced Diagnostics","volume":"15","author":"Karpi\u0144ski","year":"2025","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13534-024-00437-5","article-title":"A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays","volume":"15","author":"Tariq","year":"2025","journal-title":"Biomed. Eng. Lett."},{"issue":"10","key":"10.1016\/j.bspc.2026.109771_b6","doi-asserted-by":"crossref","first-page":"678","DOI":"10.22214\/ijraset.2022.46994","article-title":"VGG16 Based Knee Osteoarthritis Grading Using X-Ray Images","volume":"10","author":"Dhami","year":"2022","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"issue":"4","key":"10.1016\/j.bspc.2026.109771_b7","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1007\/s10067-025-07347-6","article-title":"The global burden of osteoarthritis knee: a secondary data analysis of a population-based study","volume":"44","author":"Ren","year":"2025","journal-title":"Clin. Rheumatol."},{"key":"10.1016\/j.bspc.2026.109771_b8","article-title":"Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies","volume":"29\u201330","author":"Cui","year":"2020","journal-title":"EClinicalMedicine"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b9","doi-asserted-by":"crossref","first-page":"7","DOI":"10.4103\/JCMRP.JCMRP_99_19","article-title":"Epidemiology and socioeconomic burden of osteoarthritis","volume":"8","author":"El-Hafeez","year":"2023","journal-title":"J. Curr. Med. Res. Pr."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.ocarto.2020.100135","article-title":"A machine learning-based approach to decipher multi-etiology of knee osteoarthritis onset and deterioration","volume":"3","author":"Chan","year":"2021","journal-title":"Osteoarthr. Cartil. Open"},{"key":"10.1016\/j.bspc.2026.109771_b11","article-title":"Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity","author":"Antony","year":"2019","journal-title":"Intell. Syst. Ref. Libr."},{"key":"10.1016\/j.bspc.2026.109771_b12","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-56527-3","article-title":"Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data","volume":"9","author":"Tiulpin","year":"2019","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b13","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.bbe.2021.03.002","article-title":"A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images","volume":"41","author":"Saini","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b14","article-title":"Artificial intelligence reshapes current understanding and management of osteoarthritis: A narrative review","volume":"29","author":"Yick","year":"2022","journal-title":"J. Orthop. Trauma Rehabil."},{"key":"10.1016\/j.bspc.2026.109771_b15","doi-asserted-by":"crossref","DOI":"10.1177\/1759720X231165560","article-title":"Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine\/deep learning as predictors of early osteoarthritis diagnosis and prognosis","volume":"15","author":"Martel-Pelletier","year":"2023","journal-title":"Ther. Adv. Musculoskelet. Dis."},{"issue":"5","key":"10.1016\/j.bspc.2026.109771_b16","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1007\/s00330-024-11105-8","article-title":"MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review","volume":"35","author":"Mead","year":"2024","journal-title":"Eur. Radiol."},{"issue":"14","key":"10.1016\/j.bspc.2026.109771_b17","doi-asserted-by":"crossref","first-page":"6333","DOI":"10.3390\/app14146333","article-title":"How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018\u20132024","volume":"14","author":"Touahema","year":"2024","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b18","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s00330-024-10928-9","article-title":"The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis","volume":"35","author":"Zhao","year":"2024","journal-title":"Eur. Radiol."},{"issue":"11","key":"10.1016\/j.bspc.2026.109771_b19","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1007\/s00256-023-04296-6","article-title":"Deep learning applications in osteoarthritis imaging","volume":"52","author":"Kijowski","year":"2023","journal-title":"Skelet. Radiol."},{"key":"10.1016\/j.bspc.2026.109771_b20","series-title":"2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","first-page":"345","article-title":"Machine Learning and Deep Learning for Knee Osteoarthritis Diagnosis: A Survey on Classification and Severity Grading","author":"R","year":"2024"},{"issue":"11","key":"10.1016\/j.bspc.2026.109771_b21","doi-asserted-by":"crossref","first-page":"8001","DOI":"10.21037\/qims-24-1544","article-title":"Diagnostic accuracy of magnetic resonance imaging (MRI) for symptomatic knee osteoarthritis: a scoping review","volume":"14","author":"Salamah","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.bspc.2026.109771_b22","doi-asserted-by":"crossref","first-page":"68870","DOI":"10.1109\/ACCESS.2024.3400987","article-title":"Knee Osteoarthritis Analysis Using Deep Learning and XAI on X-Rays","volume":"12","author":"Ahmed","year":"2024","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.bspc.2026.109771_b23","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.21037\/qims-22-1250","article-title":"Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge","volume":"13","author":"Li","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b24","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/TMI.2022.3206042","article-title":"Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution","volume":"42","author":"Zhuang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.109771_b25","doi-asserted-by":"crossref","first-page":"48292","DOI":"10.1109\/ACCESS.2023.3276810","article-title":"Knee Osteoarthritis Detection and Classification Using X-Rays","volume":"11","author":"Tariq","year":"2023","journal-title":"IEEE Access"},{"issue":"11","key":"10.1016\/j.bspc.2026.109771_b26","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1109\/TMI.2022.3181060","article-title":"Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction","volume":"41","author":"Hu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.bspc.2026.109771_b27","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s10278-021-00464-z","article-title":"A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative","volume":"34","author":"Deng","year":"2021","journal-title":"J. Digit. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.ostima.2022.100010","article-title":"Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition","volume":"2","author":"Kessler","year":"2022","journal-title":"Osteoarthr. Imaging"},{"key":"10.1016\/j.bspc.2026.109771_b29","series-title":"Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiative","author":"Daydar","year":"2024"},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ocarto.2025.100582","article-title":"Predictive validity of consensus-based MRI definition of osteoarthritis plus radiographic osteoarthritis for the progression of knee osteoarthritis: A longitudinal cohort study","volume":"7","author":"Xing","year":"2025","journal-title":"Osteoarthr. Cartil. Open"},{"key":"10.1016\/j.bspc.2026.109771_b31","doi-asserted-by":"crossref","DOI":"10.1007\/s11042-025-20804-3","article-title":"Ordinal classification for knee osteoarthritis x-rays using vision transformers","author":"Tariq","year":"2025","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.bspc.2026.109771_b32","doi-asserted-by":"crossref","first-page":"71326","DOI":"10.1109\/ACCESS.2023.3294542","article-title":"Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images","volume":"11","author":"Rehman","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.109771_b33","unstructured":"Radiological assessment of rheumatoid."},{"key":"10.1016\/j.bspc.2026.109771_b34","doi-asserted-by":"crossref","DOI":"10.1007\/s10439-025-03740-z","article-title":"Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative","author":"Nurmirinta","year":"2025","journal-title":"Ann. Biomed. Eng."},{"issue":"4","key":"10.1016\/j.bspc.2026.109771_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e15461","article-title":"A joint multi-modal learning method for early-stage knee osteoarthritis disease classification","volume":"9","author":"Liu","year":"2023","journal-title":"Heliyon"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b36","article-title":"Predicting total knee replacement at 2 and 5 years in osteoarthritis patients using machine learning","volume":"5","author":"Mahmoud","year":"2023","journal-title":"BMJ Surg. Interv. Health Technol."},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b37","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3390\/diagnostics11020285","article-title":"Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients","volume":"11","author":"Ntakolia","year":"2021","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b38","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.joca.2022.10.001","article-title":"The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images","volume":"31","author":"Hirvasniemi","year":"2023","journal-title":"Osteoarthr. Cartil."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b39","doi-asserted-by":"crossref","DOI":"10.1186\/s13018-022-03429-2","article-title":"Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning","volume":"17","author":"Yang","year":"2022","journal-title":"J. Orthop. Surg. Res."},{"key":"10.1016\/j.bspc.2026.109771_b40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TBME.2025.3640551","article-title":"Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis","author":"Wang","year":"2025","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b41","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-33934-1","article-title":"Prediction of total knee replacement using deep learning analysis of knee MRI","volume":"13","author":"Rajamohan","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.109771_b42","doi-asserted-by":"crossref","unstructured":"A. Sekhri, M.A. Kerkouri, A. Chetouani, M. Tliba, Y. Nasser, R. Jennane, A. Bruno, Automatic diagnosis of knee osteoarthritis severity using Swin transformer., in: International Conference on Content-Based Multimedia Indexing (CBMI), 2023, pp. 41\u201347.","DOI":"10.1145\/3617233.3617234"},{"issue":"10","key":"10.1016\/j.bspc.2026.109771_b43","doi-asserted-by":"crossref","first-page":"993","DOI":"10.3390\/diagnostics14100993","article-title":"MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis","volume":"14","author":"Touahema","year":"2024","journal-title":"Diagnostics"},{"issue":"8","key":"10.1016\/j.bspc.2026.109771_b44","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.3390\/diagnostics13081380","article-title":"Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images","volume":"13","author":"Mohammed","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.109771_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109785","article-title":"Generating synthetic past and future states of Knee Osteoarthritis radiographs using Cycle-Consistent Generative Adversarial Neural Networks","volume":"187","author":"Prezja","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b46","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.3390\/app13031658","article-title":"Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN","volume":"13","author":"M","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.109771_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2025.105574","article-title":"Deep learning-assisted 3D model for the detection and classification of knee arthritis","volume":"160","author":"Preethi","year":"2025","journal-title":"Image Vis. Comput."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b48","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-78203-x","article-title":"Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique","volume":"14","author":"Panwar","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b49","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-99460-4","article-title":"A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis","volume":"15","author":"Diab","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b50","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-23081-4","article-title":"DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification","volume":"12","author":"Prezja","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.109771_b51","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1164655","article-title":"Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative","volume":"11","author":"Yeoh","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b52","first-page":"111","article-title":"Knee osteoarthritis prediction driven by deep learning and the kellgren-lawrence grading","volume":"5","author":"Kishore","year":"2023","journal-title":"Proc. Eng. Sci."},{"key":"10.1016\/j.bspc.2026.109771_b53","doi-asserted-by":"crossref","first-page":"146698","DOI":"10.1109\/ACCESS.2024.3472654","article-title":"Knee Osteoarthritis Diagnosis With Unimodal and Multi-Modal Neural Networks: Data From the Osteoarthritis Initiative","volume":"12","author":"Teh","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b54","doi-asserted-by":"crossref","DOI":"10.1186\/s12891-024-07942-9","article-title":"Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification","volume":"25","author":"Rani","year":"2024","journal-title":"BMC Musculoskelet. Disord."},{"issue":"39","key":"10.1016\/j.bspc.2026.109771_b55","doi-asserted-by":"crossref","first-page":"86923","DOI":"10.1007\/s11042-024-19661-3","article-title":"A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification","volume":"83","author":"Malik","year":"2024","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b56","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s40011-023-01545-5","article-title":"A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography","volume":"94","author":"Malathi","year":"2024","journal-title":"Proc. Natl. Acad. Sci. India Sect. B: Biological Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b57","doi-asserted-by":"crossref","DOI":"10.1186\/s13018-024-05352-0","article-title":"Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach","volume":"19","author":"Zhang","year":"2024","journal-title":"J. Orthop. Surg. Res."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b58","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/JBHI.2021.3102090","article-title":"Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J. Biomed. Health Informat."},{"issue":"11","key":"10.1016\/j.bspc.2026.109771_b59","doi-asserted-by":"crossref","first-page":"5196","DOI":"10.3390\/app11115196","article-title":"Knee Osteoarthritis Classification Using 3D CNN and MRI","volume":"11","author":"Guida","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.109771_b60","article-title":"Classification and risk estimation of osteoarthritis using deep learning methods","volume":"35","author":"Patil","year":"2024","journal-title":"Meas.: Sensors"},{"key":"10.1016\/j.bspc.2026.109771_b61","first-page":"1","article-title":"End-To-End Prediction of Knee Osteoarthritis Progression With Multimodal Transformers","author":"Panfilov","year":"2025","journal-title":"IEEE J. Biomed. Health Informat."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b62","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10334-020-00889-7","article-title":"Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort","volume":"34","author":"Wirth","year":"2021","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b63","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/TMI.2023.3312524","article-title":"Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data","volume":"43","author":"Nguyen","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.109771_b64","doi-asserted-by":"crossref","first-page":"123757","DOI":"10.1109\/ACCESS.2024.3454374","article-title":"An Efficient Neural Network for Segmenting Multiple Joint Tissues From Knee MRI With Hyperparameter Optimization: Data From the Osteoarthritis Initiative","volume":"12","author":"Yeoh","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.109771_b65","doi-asserted-by":"crossref","first-page":"135323","DOI":"10.1109\/ACCESS.2023.3338379","article-title":"3D Efficient Multi-Task Neural Network for Knee Osteoarthritis Diagnosis Using MRI Scans: Data From the Osteoarthritis Initiative","volume":"11","author":"Yeoh","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b66","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-35832-y","article-title":"Atlas-based finite element analyses with simpler constitutive models predict personalized progression of knee osteoarthritis: data from the osteoarthritis initiative","volume":"13","author":"Mononen","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.109771_b67","series-title":"Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning","author":"Nguyen","year":"2024"},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b68","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s00256-021-03773-0","article-title":"Deep learning approach to predict pain progression in knee osteoarthritis","volume":"51","author":"Guan","year":"2022","journal-title":"Skelet. Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b69","doi-asserted-by":"crossref","DOI":"10.1186\/s13075-021-02634-4","article-title":"A deep learning method for predicting knee osteoarthritis radiographic progression from MRI","volume":"23","author":"Schiratti","year":"2021","journal-title":"Arthritis Res. Ther."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b70","doi-asserted-by":"crossref","first-page":"6925","DOI":"10.1007\/s11042-023-15484-w","article-title":"Knee osteoarthritis severity prediction using an attentive multi-scale deep convolutional neural network","volume":"83","author":"Jain","year":"2024","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b71","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3390\/diagnostics12010123","article-title":"Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI","volume":"12","author":"Almajalid","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.109771_b72","series-title":"2022 International Conference on Healthcare Engineering (ICHE)","first-page":"1","article-title":"Depthwise Separable Convolutional Neural Network for Knee Segmentation: Data from the Osteoarthritis Initiative","author":"Lai","year":"2022"},{"key":"10.1016\/j.bspc.2026.109771_b73","series-title":"Unet Architectures in Multiplanar Volumetric Segmentation - Validated on Three Knee MRI Cohorts","author":"Sengar","year":"2022"},{"key":"10.1016\/j.bspc.2026.109771_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2024.104240","article-title":"Chimeric U-Net \u2013 Modifying the standard U-Net towards explainability","volume":"338","author":"Schulze","year":"2025","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.bspc.2026.109771_b75","doi-asserted-by":"crossref","first-page":"39757","DOI":"10.1109\/ACCESS.2021.3062493","article-title":"DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging","volume":"9","author":"Karim","year":"2021","journal-title":"IEEE Access"},{"issue":"01\/02","key":"10.1016\/j.bspc.2026.109771_b76","doi-asserted-by":"crossref","first-page":"001","DOI":"10.1055\/a-2305-2115","article-title":"Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments","volume":"63","author":"Bayramoglu","year":"2024","journal-title":"Methods Inf. Med."},{"key":"10.1016\/j.bspc.2026.109771_b77","article-title":"Optimized feature selection for enhanced accuracy in knee osteoarthritis detection and severity classification with machine learning","volume":"97","author":"Bose","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b78","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3390\/life13010237","article-title":"Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review","volume":"13","author":"Almhdie-Imjabbar","year":"2023","journal-title":"Life"},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b79","doi-asserted-by":"crossref","first-page":"325","DOI":"10.21923\/jesd.1608509","article-title":"Transfer learningbased classification of knee osteoarthritis severity from x-ray images","volume":"13","author":"Mahfus","year":"2025","journal-title":"M\u00fchendislik Bilim. Ve Tasar\u0131M Derg."},{"issue":"8","key":"10.1016\/j.bspc.2026.109771_b80","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.3390\/life12081126","article-title":"Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network","volume":"12","author":"Yunus","year":"2022","journal-title":"Life"},{"issue":"18","key":"10.1016\/j.bspc.2026.109771_b81","doi-asserted-by":"crossref","first-page":"6189","DOI":"10.3390\/s21186189","article-title":"A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis","volume":"21","author":"Mahum","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.109771_b82","article-title":"Evaluating the efficacy of deep learning models for knee osteoarthritis prediction based on Kellgren-Lawrence grading system","volume":"5","author":"V","year":"2023","journal-title":"E-Prime - Adv. Electr. Eng. Electron. Energy"},{"key":"10.1016\/j.bspc.2026.109771_b83","series-title":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","first-page":"690","article-title":"A Deep Learning-Based Method for Knee Articular Cartilage Segmentation in MRI Images","author":"Zhang","year":"2021"},{"key":"10.1016\/j.bspc.2026.109771_b84","doi-asserted-by":"crossref","DOI":"10.12659\/MSM.936733","article-title":"Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model","volume":"28","author":"Tang","year":"2022","journal-title":"Med. Sci. Monit."},{"issue":"3","key":"10.1016\/j.bspc.2026.109771_b85","article-title":"Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee","volume":"4","author":"Toit","year":"2022","journal-title":"Osteoarthr. Cartil. Open"},{"key":"10.1016\/j.bspc.2026.109771_b86","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108791","article-title":"Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear","volume":"178","author":"Stirling","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b87","doi-asserted-by":"crossref","DOI":"10.1186\/s13018-024-04680-5","article-title":"Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage","volume":"19","author":"Sun","year":"2024","journal-title":"J. Orthop. Surg. Res."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b88","doi-asserted-by":"crossref","DOI":"10.1007\/s44248-025-00026-6","article-title":"Machine-learning-based diagnosis and progression analysis of knee osteoarthritis","volume":"3","author":"Jose","year":"2025","journal-title":"Discov. Data"},{"issue":"4","key":"10.1016\/j.bspc.2026.109771_b89","doi-asserted-by":"crossref","first-page":"183","DOI":"10.3390\/info15040183","article-title":"A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis","volume":"15","author":"Raza","year":"2024","journal-title":"Information"},{"key":"10.1016\/j.bspc.2026.109771_b90","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.bspc.2026.109771_b91","series-title":"International Conference on Learning Representations","article-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","author":"Dosovitskiy","year":"2020"},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b92","doi-asserted-by":"crossref","first-page":"71","DOI":"10.35784\/acs-2022-14","article-title":"Knee joint osteoarthritis diagnosis based on selected acoustic signal discriminants using machine learning","volume":"18","author":"KARPI\u0143.SKI","year":"2022","journal-title":"Appl. Comput. Sci."},{"issue":"5","key":"10.1016\/j.bspc.2026.109771_b93","doi-asserted-by":"crossref","first-page":"19","DOI":"10.12913\/22998624\/189512","article-title":"Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography","volume":"18","author":"Machrowska","year":"2024","journal-title":"Adv. Sci. Technol. Res. J."},{"issue":"2","key":"10.1016\/j.bspc.2026.109771_b94","doi-asserted-by":"crossref","first-page":"90","DOI":"10.35784\/acs-2024-18","article-title":"Application of eemd-dfa algorithms and ann classification for detection of knee osteoarthritis using vibroarthrography","volume":"20","author":"MACHROWSKA","year":"2024","journal-title":"Appl. Comput. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.109771_b95","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies in medicine","volume":"25","author":"He","year":"2019","journal-title":"Nature Med."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426003253?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426003253?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T15:53:38Z","timestamp":1779033218000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426003253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":95,"alternative-id":["S1746809426003253"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109771","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning in knee osteoarthritis: A task-based systematic review of recent advances","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109771","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109771"}}