{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:06:33Z","timestamp":1783055193230,"version":"3.54.6"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031243516","type":"print"},{"value":"9783031243523","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-24352-3_8","type":"book-chapter","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T16:04:38Z","timestamp":1673971478000},"page":"97-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Review on: Deep Learning and Computer Intelligent Techniques Using X-Ray Imaging for the Early Detection of Knee Osteoarthritis"],"prefix":"10.1007","author":[{"given":"Ravindra D.","family":"Kale","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarika","family":"Khandelwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Bhat, A.Y., Suhasini, A.: Normal and abnormal detection for knee osteoarthritis using machine learning techniques. Int. J. Recent Technol. Eng. (IJRTE) 8(2), (2019). ISSN: 2277\u20133878, Retrieval Number: B3733078219\/19\u00a9BEIESP. https:\/\/doi.org\/10.35940\/ijrte.B3733.078219 (2019)","DOI":"10.35940\/ijrte.B3733.078219"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"611","DOI":"10.3390\/diagnostics12030611","volume":"12","author":"SM Ahmed","year":"2022","unstructured":"Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: from conventional methods to deep learning. Diagnostics 12, 611 (2022). https:\/\/doi.org\/10.3390\/diagnostics12030611","journal-title":"Diagnostics"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Yanfei, W., et al.: Causal discovery in radiographic markers of knee osteoarthritis and prediction for knee osteoarthritis severity with attention\u2013long short-Term Memory. J. Front. Public Health, 8, (2020). https:\/\/www.frontiersin.org\/article\/10.3389\/fpubh.2020.604654, https:\/\/doi.org\/10.3389\/fpubh.2020.604654, ISSN=2296\u20132565 (2020)","DOI":"10.3389\/fpubh.2020.604654"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Kokkotis, C., Moustakidis, S., Papageorgiou, E., Giakas, G., Tsaopoulos, D.E.: Machine learning in knee osteoarthritis: a review, Osteoarthritis and Cartilage Open, 2(3), 100069 (2020). ISSN 2665 9131, https:\/\/doi.org\/10.1016\/j.ocarto.2020.100069. (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2665913120300583) (2020)","DOI":"10.1016\/j.ocarto.2020.100069"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s13246-022-01106-6","volume":"45","author":"C Kokkotis","year":"2022","unstructured":"Kokkotis, C., Ntakolia, C., Moustakidis, S., et al.: Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Phys. Eng. Sci. Med. 45, 219\u2013229 (2022). https:\/\/doi.org\/10.1007\/s13246-022-01106-6","journal-title":"Phys. Eng. Sci. Med."},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Roemer, F., et al.: State of the art: imaging of osteoarthritis\u2014revisited. Radiology 296(192498), (2020). https:\/\/doi.org\/10.1148\/radiol.2020192498","DOI":"10.1148\/radiol.2020192498"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Hossein, B., et al.: A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening. Therapeutic Advances in Musculoskeletal Disease, Jan. 2021 (2021). https:\/\/doi.org\/10.1177\/1759720X21993254","DOI":"10.1177\/1759720X21993254"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Zeng, K., et al.: Multicentre study using machine learning methods in clinical diagnosis of knee osteoarthritis. J. Healthcare Eng. 2021, 1765404, 12 (2021). https:\/\/doi.org\/10.1155\/2021\/1765404","DOI":"10.1155\/2021\/1765404"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"H\u00fcgle, M., Omoumi, P., Laar, J., Boedecker, J., H\u00fcgle, T.: Applied machine learning and artificial intelligence in rheumatology. Rheumatol. Adv. Pract. 4, (2020). https:\/\/doi.org\/10.1093\/rap\/rkaa005","DOI":"10.1093\/rap\/rkaa005"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Mahum, R., et al.: A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors 21, 6189 (2021). https:\/\/doi.org\/10.3390\/s21186189 (2020)","DOI":"10.3390\/s21186189"},{"key":"8_CR11","unstructured":"Xiao, Y.: Using machine learning tools to predict the severity of osteoarthritis based on knee XRay data (2020). Master's Theses (2009 -). 582. https:\/\/epublications.marquette.edu\/theses_open\/582"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Binvignat, M., Pedoia, V., Butte, A.J., et al.: Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open 2022; 8:e001998. https:\/\/doi.org\/10.1136\/rmdopen-2021-001998 (2022)","DOI":"10.1136\/rmdopen-2021-001998"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Bayramoglu, N., Tiulpin, A., Hirvasniemi, J., Nieminen, M.T., Saarakkala, S.: Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis, Osteoarthritis Cartilage 28(7), 941\u2013952 (2020). ISSN10634584, https:\/\/doi.org\/10.1016\/j.joca.2020.03.006. (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1063458420309481) (2020)","DOI":"10.1016\/j.joca.2020.03.006"},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Teoh, Y.X., et al.: Discovering knee osteoarthritis imaging features for diagnosis and prognosis: review of manual imaging grading and machine learning approaches. J. Healthcare Eng. 2022, 4138666, 19 (2022). https:\/\/doi.org\/10.1155\/2022\/4138666","DOI":"10.1155\/2022\/4138666"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Brahim, A., et al.: A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: data from the OsteoArthritis Initiative, Computerized Medical Imaging and Graphics, Vol. 73, (2019), pp. 11\u201318, ISSN 0895\u20136111, https:\/\/doi.org\/10.1016\/j.compmedimag.2019.01.07","DOI":"10.1016\/j.compmedimag.2019.01.07"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/s13075-022-02743-8","volume":"24","author":"A Almhdie-Imjabbar","year":"2022","unstructured":"Almhdie-Imjabbar, A., Nguyen, K.L., Toumi, H., et al.: Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts. Arthritis Res Ther 24, 66 (2022). https:\/\/doi.org\/10.1186\/s13075-022-02743-8","journal-title":"Arthritis Res Ther"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Sheng, B., et al.: Identification of knee osteoarthritis based on Bayesian network: a pilot study (Preprint). https:\/\/doi.org\/10.2196\/preprints.13562 (2019)","DOI":"10.2196\/preprints.13562"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Saini, D., Chand, T., Chouhan, D.K., Prakash, M.: A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images, Biocybernetics and Biomedical Engineering, Vol. 41, Iss. 2, 2021, pp. 419\u2013444, ISSN 0208\u20135216, https:\/\/doi.org\/10.1016\/j.bbe.2021.03.02","DOI":"10.1016\/j.bbe.2021.03.02"},{"issue":"262","key":"8_CR19","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1186\/s13075-021-02634-4","volume":"23","author":"JB Schiratti","year":"2021","unstructured":"Schiratti, J.B., Dubois, R., Herent, P., et al.: A deep learning method for predicting knee osteoarthritis radiographic progression from MRI. Arthritis. Res. Ther. 23(262), 2021 (2021). https:\/\/doi.org\/10.1186\/s13075-021-02634-4","journal-title":"Arthritis. Res. Ther."},{"key":"8_CR20","unstructured":"Hernandez Abasolo, K.: Detection of knee osteoarthritis severity using a fusion of machine and deep learning models. Diss. Dublin, National College of Ireland (2021)"},{"key":"8_CR21","doi-asserted-by":"publisher","unstructured":"Thomas, K., et al.: Automated classification of radiographic knee osteoarthritis severity using deep neural networks. Radiol.: Artif. Intelligence. 2. e190065. https:\/\/doi.org\/10.1148\/ryai.2020190065 (2020)","DOI":"10.1148\/ryai.2020190065"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Ribas, L.C., Riad, R., Jennane, R., Bruno, O.M.: A complex network based approach for knee Osteoarthritis detection: data from the osteoarthritis initiative, biomedical signal processing and control, Vol. 71, Part A, 2022, 103133, ISSN17468094, https:\/\/doi.org\/10.1016\/j.bspc.2021.103133. (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809421007308) (2022)","DOI":"10.1016\/j.bspc.2021.103133"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Bayramoglu, N., Tiulpin, A., Hirvasniemi, J., Nieminen, M.T., Saarakkala, S.: Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis, Osteoarthritis and Cartilage, Vol. 28, Iss. 7, pp. 941\u2013952 (2020), ISSN 1063\u20134584, https:\/\/doi.org\/10.1016\/j.joca.2020.03.06","DOI":"10.1016\/j.joca.2020.03.06"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Yeoh, P.S.Q., et al.: Emergence of deep learning in knee osteoarthritis diagnosis. Comput. Intell. Neurosci. 2021, 4931437, 20 (2021). https:\/\/doi.org\/10.1155\/2021\/4931437","DOI":"10.1155\/2021\/4931437"},{"key":"8_CR25","unstructured":"Revathy, B., et al.: A review on investigation and catagorization of rheumatoid arthritis and osteoarthritis using image processing techniques. Annals of the Romanian Society for Cell Biology 25.4 (2021) 2275\u20132290 (2021)"},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1073\/pnas.1917405117","volume":"117","author":"S Kundu","year":"2020","unstructured":"Kundu, S., et al.: Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning. Proc. Natl. Acad. Sci. U.S.A. 117, 2020 (2020). https:\/\/doi.org\/10.1073\/pnas.1917405117","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"8_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.591827","volume":"7","author":"SS Gornale","year":"2020","unstructured":"Gornale, S.S.: Automatic detection and classification of knee osteoarthritis using Hu\u2019s invariant moments. Front. Robot. AI 7, 591827 (2020). https:\/\/doi.org\/10.3389\/frobt.2020.591827","journal-title":"Front. Robot. AI"},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Du, Y., Almajalid, R., Shan, J., Zhang, M.: A novel method to predict knee osteoarthritis progression on MRI using machine learning methods. IEEE Transactions on NanoBioscience p. 1 (2019). https:\/\/doi.org\/10.1109\/TNB.2018.2840082","DOI":"10.1109\/TNB.2018.2840082"},{"issue":"4","key":"8_CR29","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/s11547-022-01476-7","volume":"127","author":"SS Abdullah","year":"2022","unstructured":"Abdullah, S.S., Rajasekaran, M.P.: Automatic detection and classification of knee osteoarthritis using deep learning approach. Radiol. Med. (Torino) 127(4), 398\u2013406 (2022). https:\/\/doi.org\/10.1007\/s11547-022-01476-7","journal-title":"Radiol. Med. (Torino)"},{"key":"8_CR30","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3390\/diagnostics11020285","volume":"11","author":"C Ntakolia","year":"2021","unstructured":"Ntakolia, C., Kokkotis, C., Moustakidis, S., Tsaopoulos, D.: Prediction of joint space narrowing progression in knee osteoarthritis patients. Diagnostics 11, 285 (2021). https:\/\/doi.org\/10.3390\/diagnostics11020285","journal-title":"Diagnostics"},{"key":"8_CR31","doi-asserted-by":"publisher","first-page":"6797","DOI":"10.3390\/app10196797","volume":"10","author":"C Kokkotis","year":"2020","unstructured":"Kokkotis, C., Moustakidis, S., Giakas, G., Tsaopoulos, D.: Identifcation of risk factors and machine learning-based predictionmodels for knee osteoarthritis patients. Appl. Sci. 10, 6797 (2020). https:\/\/doi.org\/10.3390\/app10196797","journal-title":"Appl. Sci."},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Sharma, M., Khandelwal, S.: Image fusion on coloured and gray scale multi focus images by using hybrid DWT-DCT. Int. J. Comput. Appl. (0975 \u2013 8887) 152, 9 (2016)","DOI":"10.5120\/ijca2016911861"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Gornale, S.S., Patravali, P.U., Hiremath, P.S.: Automatic detection and classification of knee osteoarthritis using Hu\u2019s invariant moments. Front. Robot. AI 2020, 7, 591827 (2020)","DOI":"10.3389\/frobt.2020.591827"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Mohamed, A.R., Kingsbury, B., Ramabhadran, B.: Deep convolutional neural networks for LVCSR. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26\u201331 May 2013, pp. 8614\u20138618 (2013)","DOI":"10.1109\/ICASSP.2013.6639347"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Song, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. 2017, 9314740 (2017)","DOI":"10.1155\/2017\/8314740"},{"issue":"49","key":"8_CR36","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jvcir.2017.09.008","volume":"2017","author":"NAB Mary","year":"2017","unstructured":"Mary, N.A.B., Dharma, D.: Coral reef image classification employing improved LDP for feature extraction. J. Vis. Commun. Image Represent. 2017(49), 225\u2013242 (2017)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"8_CR37","doi-asserted-by":"publisher","unstructured":"Shivanand Gornale, P.P.: Digital Knee X-ray Images. https:\/\/doi.org\/10.17632\/t9ndx37v5h.1#folder-18a3659a-1fa2-4340-b7bb-526fb81006f6, 23 June 2020","DOI":"10.17632\/t9ndx37v5h.1#folder-18a3659a-1fa2-4340-b7bb-526fb81006f6"}],"container-title":["Communications in Computer and Information Science","Machine Learning, Image Processing, Network Security and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24352-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T16:16:49Z","timestamp":1673972209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24352-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031243516","9783031243523"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24352-3_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"18 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIND","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Image Processing, Network Security and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mind2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.manit.ac.in\/mind2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"399","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}