{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:46:58Z","timestamp":1782251218991,"version":"3.54.5"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thekneejournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Knee"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.knee.2026.104365","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T14:16:43Z","timestamp":1773065803000},"page":"104365","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations"],"prefix":"10.1016","volume":"61","author":[{"given":"M.","family":"Ganesh Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.","family":"Bharathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Senthil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Koteswara Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Rajya Lakshmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knee.2026.104365_b0005","doi-asserted-by":"crossref","DOI":"10.1002\/ima.23057","article-title":"Automatic detection of knee osteoarthritis grading using artificial intelligence\u2010based methods","volume":"34","author":"Yildirim","year":"2024","journal-title":"Int J Imaging Syst Technol"},{"key":"10.1016\/j.knee.2026.104365_b0010","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s13246-023-01256-1","article-title":"A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images","volume":"46","author":"Nasser","year":"2023","journal-title":"Phys Eng Sci Med"},{"key":"10.1016\/j.knee.2026.104365_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109641","article-title":"Knee osteoarthritis severity detection using deep inception transfer learning","volume":"186","author":"Sohail","year":"2025","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.knee.2026.104365_b0020","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s42979-022-01644-6","article-title":"Detection of knee osteoarthritis stages using convolutional neural network","volume":"4","author":"Upadhyay","year":"2023","journal-title":"SN Comput Sci"},{"key":"10.1016\/j.knee.2026.104365_b0025","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.ard.2025.01.012","article-title":"Deep learning\u2013based clustering for endotyping and post-arthroplasty response classification using knee osteoarthritis multiomic data","volume":"84","author":"Rockel","year":"2025","journal-title":"Ann Rheum Dis"},{"key":"10.1016\/j.knee.2026.104365_b0030","doi-asserted-by":"crossref","first-page":"S215","DOI":"10.1016\/j.arth.2023.02.055","article-title":"Deep learning phenotype automation and cohort analyses of 1946 knees using the coronal plane alignment of the knee classification","volume":"38","author":"Steele","year":"2023","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.knee.2026.104365_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103863","article-title":"A novel computer-assisted diagnosis method of knee osteoarthritis based on multivariate information and deep learning model","volume":"133","author":"Song","year":"2023","journal-title":"Digit Signal Process"},{"key":"10.1016\/j.knee.2026.104365_b0040","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1186\/s12891-023-06951-4","article-title":"Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis","volume":"24","author":"Yoon","year":"2023","journal-title":"BMC Musculoskelet Disord"},{"key":"10.1016\/j.knee.2026.104365_b0045","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00521-020-05459-5","article-title":"Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness","volume":"35","author":"Moustakidis","year":"2023","journal-title":"Neural Comput Applic"},{"key":"10.1016\/j.knee.2026.104365_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107807","article-title":"Stratifying knee osteoarthritis features through multitask deep hybrid learning: data from the osteoarthritis initiative","volume":"242","author":"Teoh","year":"2023","journal-title":"Comp Methods Programs Biomed"},{"key":"10.1016\/j.knee.2026.104365_b0055","series-title":"Seminars in Arthritis and Rheumatism","article-title":"Predicting the onset of end-stage knee osteoarthritis over two-and five-years using machine learning","author":"Salis","year":"2024"},{"issue":"11","key":"10.1016\/j.knee.2026.104365_b0060","doi-asserted-by":"crossref","first-page":"22283","DOI":"10.1109\/ACCESS.2023.3247502","article-title":"Knee osteoarthritis detection using an improved CenterNet with pixel-wise voting scheme","volume":"22","author":"Aladhadh","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.knee.2026.104365_b0065","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":"Multimed Tools Appl"},{"key":"10.1016\/j.knee.2026.104365_b0070","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1186\/s13018-023-03837-y","article-title":"Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis","volume":"18","author":"Cui","year":"2023","journal-title":"J Orthop Surg Res"},{"key":"10.1016\/j.knee.2026.104365_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.medengphy.2023.103957","article-title":"IJES-OA net: a residual neural network to classify knee osteoarthritis from radiographic images based on the edges of the intra-joint spaces","volume":"113","author":"Farajzadeh","year":"2023","journal-title":"Med Eng Phys"},{"key":"10.1016\/j.knee.2026.104365_b0080","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1007\/s00167-022-07213-x","article-title":"Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population\u2010level analysis using deep learning","volume":"31","author":"Kunze","year":"2023","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"10.1016\/j.knee.2026.104365_b0085","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.knee.2026.104365_b0090","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12984-024-01337-6","article-title":"Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices","volume":"21","author":"Li","year":"2024","journal-title":"J Neuroeng Rehabil"},{"key":"10.1016\/j.knee.2026.104365_b0095","doi-asserted-by":"crossref","first-page":"9763","DOI":"10.1007\/s00521-023-08214-8","article-title":"Improving knee osteoarthritis classification using multimodal intermediate fusion of X-ray, MRI, and clinical information","volume":"35","author":"Guida","year":"2023","journal-title":"Neural Comput Applic"},{"key":"10.1016\/j.knee.2026.104365_b0100","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1007\/s10439-024-03549-2","article-title":"Knee-loading predictions with neural networks improve finite element modeling classifications of knee osteoarthritis: data from the osteoarthritis initiative","volume":"52","author":"Paz","year":"2024","journal-title":"Ann Biomed Eng"},{"key":"10.1016\/j.knee.2026.104365_b0105","article-title":"EHA-LNN: Optimized light gradient-boosting machine enabled neural network for cancer detection using mammography","volume":"105","author":"Kumar","year":"2025","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.knee.2026.104365_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108125","article-title":"Optimized dynamic global structure enhanced multi-channel graph neural network based automatic cataract disease classification","volume":"110","author":"Balu","year":"2025","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.knee.2026.104365_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112967","article-title":"Smart office automation using multi-dimensional attention spiking neural network for face recognition in internet of things","author":"Taluja","year":"2025","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.knee.2026.104365_b0120","article-title":"Citrus plant classification using Gated Fusion Adaptive Graph Neural Network with Harbor Seal Whiskers Optimization Algorithm","volume":"329","author":"Vijayakumaran","year":"2025","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.knee.2026.104365_bib181","doi-asserted-by":"crossref","first-page":"108928","DOI":"10.1016\/j.bspc.2025.108928","article-title":"AI-based tuberculosis detection and localization in chest X-rays using a Binarized Simplicial Convolutional Neural Network for efficient and accurate diagnosis","volume":"113","author":"Surulivelu","year":"2026","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.knee.2026.104365_bib182","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-30366-x","article-title":"Gene expression profiling and predictive modeling of cancer biomarkers using machine learning and IoT-Enabled biosensors","author":"KalaiSelvi","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.knee.2026.104365_b0125","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.eij.2023.03.005","article-title":"A fully automatic fine tuned deep learning model for knee osteoarthritis detection and progression analysis","volume":"24","author":"Abd El-Ghany","year":"2023","journal-title":"Egyptian Informatics Journal"},{"key":"10.1016\/j.knee.2026.104365_b0130","doi-asserted-by":"crossref","first-page":"41497","DOI":"10.1007\/s11042-021-10557-0","article-title":"Knee osteoarthritis severity classification with ordinal regression module","volume":"81","author":"Yong","year":"2022","journal-title":"Multimed Tools Appl"},{"issue":"11","key":"10.1016\/j.knee.2026.104365_b0135","doi-asserted-by":"crossref","first-page":"48292","DOI":"10.1109\/ACCESS.2023.3276810","article-title":"Knee osteoarthritis detection and classification using X-rays","volume":"16","author":"Tariq","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.knee.2026.104365_b0140","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":"Multimed Tools Appl"},{"key":"10.1016\/j.knee.2026.104365_b0145","article-title":"Evaluating the efficacy of deep learning models for knee osteoarthritis prediction based on Kellgren\u2013Lawrence grading system","volume":"5","author":"Kalpana","year":"2023","journal-title":"e-Prime-Advances in Electrical Engineering, Electronics and Energy"},{"key":"10.1016\/j.knee.2026.104365_b0150","article-title":"Classification and risk estimation of osteoarthritis using deep learning methods","volume":"35","author":"Patil","year":"2024","journal-title":"Measur Sens"},{"key":"10.1016\/j.knee.2026.104365_b0155","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.knee.2026.104365_b0160","unstructured":"Knee Osteoarthritis Dataset with Severity Grading. Available at: https:\/\/www.kaggle.com\/datasets\/shashwatwork\/knee-osteoarthritis-dataset-with-severity."},{"key":"10.1016\/j.knee.2026.104365_b0165","first-page":"1","article-title":"Smooth deep learning magnetotelluric inversion based on physics-informed Swin transformer and multiwindow Savitzky\u2013Golay filter","volume":"61","author":"Liu","year":"2023","journal-title":"IEEE Trans Geosci Rem Sens"},{"key":"10.1016\/j.knee.2026.104365_b0170","article-title":"FA-ResNet: Feature affine residual network for large-scale point cloud segmentation","volume":"118","author":"Zhan","year":"2023","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"10.1016\/j.knee.2026.104365_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106147","article-title":"Ci-GNN: a granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis","volume":"172","author":"Zheng","year":"2024","journal-title":"Neural Netw"},{"key":"10.1016\/j.knee.2026.104365_b0180","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-024-10729-y","article-title":"Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems","volume":"57","author":"Fu","year":"2024","journal-title":"Artif Intell Rev"}],"container-title":["The Knee"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968016026000438?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968016026000438?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T20:53:04Z","timestamp":1782247984000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0968016026000438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":38,"alternative-id":["S0968016026000438"],"URL":"https:\/\/doi.org\/10.1016\/j.knee.2026.104365","relation":{},"ISSN":["0968-0160"],"issn-type":[{"value":"0968-0160","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations","name":"articletitle","label":"Article Title"},{"value":"The Knee","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knee.2026.104365","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104365"}}