{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:45:29Z","timestamp":1783489529852,"version":"3.55.0"},"reference-count":74,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"content-version":"vor","delay-in-days":313,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004386","name":"Universiti Malaya","doi-asserted-by":"publisher","award":["FRGS\/1\/2018\/TK04\/UM\/02\/9"],"award-info":[{"award-number":["FRGS\/1\/2018\/TK04\/UM\/02\/9"]}],"id":[{"id":"10.13039\/501100004386","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002671","name":"Universiti Tunku Abdul Rahman","doi-asserted-by":"publisher","award":["IPSR\/RMC\/UTARRF\/2020-C1\/H02"],"award-info":[{"award-number":["IPSR\/RMC\/UTARRF\/2020-C1\/H02"]}],"id":[{"id":"10.13039\/501100002671","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3\u2010dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state\u2010of\u2010the\u2010art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.<\/jats:p>","DOI":"10.1155\/2021\/4931437","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T06:05:35Z","timestamp":1636610735000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["Emergence of Deep Learning in Knee Osteoarthritis Diagnosis"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-4479","authenticated-orcid":false,"given":"Pauline Shan Qing","family":"Yeoh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8602-0533","authenticated-orcid":false,"given":"Khin Wee","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-1196","authenticated-orcid":false,"given":"Siew Li","family":"Goh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0471-3820","authenticated-orcid":false,"given":"Khairunnisa","family":"Hasikin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9657-8311","authenticated-orcid":false,"given":"Yan Chai","family":"Hum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0263-6358","authenticated-orcid":false,"given":"Yee Kai","family":"Tee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6970-2719","authenticated-orcid":false,"given":"Samiappan","family":"Dhanalakshmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28251"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2985861"},{"key":"e_1_2_13_3_2","doi-asserted-by":"crossref","unstructured":"RajA. VishwanathanS. AjaniB. KrishnanK. andAgarwalH. Automatic knee cartilage segmentation using fully volumetric convolutional neural networks for evaluation of osteoarthritis Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) April 2018 Washington DC USA IEEE 851\u2013854 https:\/\/doi.org\/10.1109\/isbi.2018.8363705 2-s2.0-85048077986.","DOI":"10.1109\/ISBI.2018.8363705"},{"key":"e_1_2_13_4_2","doi-asserted-by":"crossref","unstructured":"ChristodoulouE. MoustakidisS. PapandrianosN. TsaopoulosD. andPapageorgiouE. Exploring deep learning capabilities in knee osteoarthritis case study for classification Proceedings of the 2019 10th International Conference on Information Intelligence Systems and Applications (IISA) July 2019 Patras Greece IEEE 1\u20136 https:\/\/doi.org\/10.1109\/iisa.2019.8900714.","DOI":"10.1109\/IISA.2019.8900714"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0098-3"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/jor.24811"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2020.02.478"},{"key":"e_1_2_13_8_2","first-page":"1","article-title":"From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research","volume":"2020","author":"Gan H. S.","year":"2021","journal-title":"Artificial Intelligence Review"},{"key":"e_1_2_13_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16071281"},{"key":"e_1_2_13_10_2","first-page":"S88","article-title":"Magnetic resonance imaging assessment of knee osteoarthritis: current and developing new concepts and techniques","volume":"37","author":"Hayashi D.","year":"2019","journal-title":"Clinical & Experimental Rheumatology"},{"key":"e_1_2_13_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2020.01.010"},{"key":"e_1_2_13_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2020.04.059"},{"key":"e_1_2_13_13_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0039-3400264"},{"key":"e_1_2_13_14_2","first-page":"1","article-title":"Assessment of knee pain from mr imaging using a convolutional siamese network","volume":"30","author":"Chang G. H.","year":"2020","journal-title":"European Radiology"},{"key":"e_1_2_13_15_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172986"},{"key":"e_1_2_13_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-20132-7"},{"key":"e_1_2_13_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9103341"},{"key":"e_1_2_13_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.02.386"},{"key":"e_1_2_13_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.02.399"},{"key":"e_1_2_13_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26246"},{"key":"e_1_2_13_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-63395-9"},{"key":"e_1_2_13_22_2","first-page":"1","article-title":"Deep learning approach to predict pain progression in knee osteoarthritis","volume":"8","author":"Guan B.","year":"2021","journal-title":"Skeletal Radiology"},{"key":"e_1_2_13_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.3017007"},{"key":"e_1_2_13_24_2","doi-asserted-by":"publisher","DOI":"10.1002\/jor.24151"},{"key":"e_1_2_13_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26991"},{"key":"e_1_2_13_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10110932"},{"key":"e_1_2_13_27_2","doi-asserted-by":"crossref","unstructured":"XuZ.andNiethammerM. Deepatlas: joint semi-supervised learning of image registration and segmentation Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention October 2019 Lima Peru Springer 420\u2013429 https:\/\/doi.org\/10.1007\/978-3-030-32245-8_47.","DOI":"10.1007\/978-3-030-32245-8_47"},{"key":"e_1_2_13_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101851"},{"key":"e_1_2_13_29_2","doi-asserted-by":"crossref","unstructured":"WahyuningrumR. T. AnifahL. PurnamaI. K. E. andPurnomoM. H. A new approach to classify knee osteoarthritis severity from radiographic images based on CNN-LSTM method Proceedings of the 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) October 2019 Morioka Japan IEEE 1\u20136.","DOI":"10.1109\/ICAwST.2019.8923284"},{"key":"e_1_2_13_30_2","doi-asserted-by":"crossref","unstructured":"AntonyJ. McGuinnessK. O\u2019ConnorN. E. andMoranK. Quantifying radiographic kneeosteoarthritis severity using deep convolutional neural networks Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR) December 2016 Canc\u00fan Mexico IEEE 1195\u20131200 https:\/\/doi.org\/10.1109\/icpr.2016.7899799 2-s2.0-85019158805.","DOI":"10.1109\/ICPR.2016.7899799"},{"key":"e_1_2_13_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10334-020-00889-7"},{"key":"e_1_2_13_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27969"},{"key":"e_1_2_13_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27920"},{"key":"e_1_2_13_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.06.002"},{"key":"e_1_2_13_35_2","doi-asserted-by":"crossref","unstructured":"MemariN.andMoghbelM. Computer-aided diagnosis (CAD) of knee osteoarthritis based on magnetic resonance imaging for quantitative pathogenesis analysis and visualization Proceedings of the 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE) April 2020 Piscataway NJ USA IEEE 192\u2013197 https:\/\/doi.org\/10.1109\/iscaie47305.2020.9108837.","DOI":"10.1109\/ISCAIE47305.2020.9108837"},{"key":"e_1_2_13_36_2","doi-asserted-by":"crossref","unstructured":"PanfilovE. TiulpinA. KleinS. NieminenM. T. andSaarakkalaS. Improving robustness of deep learning based knee MRI segmentation: mixup and adversarial domain adaptation Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops October 2019 Seoul Korea.","DOI":"10.1109\/ICCVW.2019.00057"},{"key":"e_1_2_13_37_2","doi-asserted-by":"crossref","unstructured":"KompellaG. AnticoM. andSasazawaF. Segmentation of femoral cartilage from knee ultrasound images using mask R-CNN Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) July 2019 Berlin Germany IEEE 966\u2013969 https:\/\/doi.org\/10.1109\/embc.2019.8857645.","DOI":"10.1109\/EMBC.2019.8857645"},{"key":"e_1_2_13_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2014.04.008"},{"key":"e_1_2_13_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-287-540-2_1"},{"key":"e_1_2_13_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-017-1710-2"},{"key":"e_1_2_13_41_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12045"},{"key":"e_1_2_13_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2020.12.019"},{"key":"e_1_2_13_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.11.009"},{"key":"e_1_2_13_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2018.12.009"},{"key":"e_1_2_13_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2021.03.002"},{"key":"e_1_2_13_46_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172322"},{"key":"e_1_2_13_47_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.600049"},{"key":"e_1_2_13_48_2","doi-asserted-by":"crossref","unstructured":"PrasoonA. PetersenK. IgelC. LauzeF. DamE. andNielsenM. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention September 2013 Toronto Canada 246\u2013253 https:\/\/doi.org\/10.1007\/978-3-642-40763-5_31 2-s2.0-84885933775.","DOI":"10.1007\/978-3-642-40763-5_31"},{"key":"e_1_2_13_49_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28111"},{"key":"e_1_2_13_50_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26841"},{"key":"e_1_2_13_51_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27229"},{"key":"e_1_2_13_52_2","doi-asserted-by":"crossref","unstructured":"RonnebergerO. FischerP. andBroxT. U-net: convolutional networks for biomedical image segmentation Proceedings of the International Conference on Medical image Computing and Computer-Assisted Intervention September 2015 Strasbourg France 234\u2013241 https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 2-s2.0-84951834022.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_13_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.02.396"},{"key":"e_1_2_13_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.02.398"},{"key":"e_1_2_13_55_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0255-1"},{"key":"e_1_2_13_56_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-01192-7"},{"key":"e_1_2_13_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2020.02.484"},{"key":"e_1_2_13_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-02096-9"},{"key":"e_1_2_13_59_2","doi-asserted-by":"crossref","unstructured":"ZhangB. TanJ. ChoK. ChangG. andDenizC. M. Attention-based cnn for kl grade classification: data from the osteoarthritis initiative Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) April 2020 Iowa City Iowa IEEE 731\u2013735 https:\/\/doi.org\/10.1109\/isbi45749.2020.9098456.","DOI":"10.1109\/ISBI45749.2020.9098456"},{"key":"e_1_2_13_60_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192091"},{"key":"e_1_2_13_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2019.02.800"},{"key":"e_1_2_13_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101793"},{"key":"e_1_2_13_63_2","first-page":"1","article-title":"Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness","volume":"10","author":"Moustakidis S.","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"e_1_2_13_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3034418"},{"key":"e_1_2_13_65_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26872"},{"key":"e_1_2_13_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2018.02.907"},{"key":"e_1_2_13_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.11.009"},{"key":"e_1_2_13_68_2","doi-asserted-by":"crossref","unstructured":"TackA.andZachowS. Accurate automated volumetry of cartilage of the knee using convolutional neural networks: data from the osteoarthritis initiative Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) April 2019 Venice Italy IEEE 40\u201343.","DOI":"10.1109\/ISBI.2019.8759201"},{"key":"e_1_2_13_69_2","article-title":"Towards understanding mechanistic subgroups of osteoarthritis: 8\u2010year cartilage thickness trajectory analysis","volume":"54","author":"Iriondo C.","year":"2020","journal-title":"Journal of Orthopaedic Research\u00ae"},{"key":"e_1_2_13_70_2","doi-asserted-by":"crossref","unstructured":"TanC. YanZ. ZhangS. LiK. andMetaxasD. N. Collaborative multi-agent learning for MR knee articular cartilage segmentation Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention September 2019 Granada Spain Springer 282\u2013290 https:\/\/doi.org\/10.1007\/978-3-030-32245-8_32.","DOI":"10.1007\/978-3-030-32245-8_32"},{"key":"e_1_2_13_71_2","doi-asserted-by":"crossref","unstructured":"LeeH. HongH. andKimJ. A novel method for cartilage segmentation of knee MRI via deep segmentation networks with bone-cartilage-complex modeling Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) April 2018 Washington DC USA IEEE 1538\u20131541.","DOI":"10.1109\/ISBI.2018.8363866"},{"key":"e_1_2_13_72_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.27266"},{"key":"e_1_2_13_73_2","doi-asserted-by":"crossref","unstructured":"HeinrichM. P. OktayO. andBouteldjaN. Obelisk-one kernel to solve nearly everything: unified 3d binary convolutions for image analysis Proceedings of the 1st Conference on Medical Imaging With Deep Learning (MIDL 2018) October 2018 Lima Peru.","DOI":"10.1016\/j.media.2019.02.006"},{"key":"e_1_2_13_74_2","doi-asserted-by":"publisher","DOI":"10.1166\/asl.2018.11156"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4931437.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4931437.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4931437","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T12:26:03Z","timestamp":1722947163000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4931437"}},"subtitle":[],"editor":[{"given":"Bai Yuan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4931437"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4931437","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-08-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4931437"}}