{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T12:23:29Z","timestamp":1759580609449,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031530845"},{"type":"electronic","value":"9783031530852"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53085-2_11","type":"book-chapter","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:02:40Z","timestamp":1706551360000},"page":"127-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Make and Model Identification of Reverse Shoulder Implants Using Deep Learning Methodology"],"prefix":"10.1007","author":[{"given":"Ved Prakash","family":"Dubey","sequence":"first","affiliation":[]},{"given":"A.","family":"Ramanathan","sequence":"additional","affiliation":[]},{"given":"Senthilvelan","family":"Rajagopalan","sequence":"additional","affiliation":[]},{"given":"C.","family":"Malathy","sequence":"additional","affiliation":[]},{"given":"M.","family":"Gayathri","sequence":"additional","affiliation":[]},{"given":"Vineet","family":"Batta","sequence":"additional","affiliation":[]},{"given":"Srinath","family":"Kamineni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.csbj.2020.04.005","volume":"18","author":"G Urban","year":"2020","unstructured":"Urban, G., Porhemmat, S., Stark, M., Feeley, B., Okada, K., Baldi, P.: Classifying shoulder implants in X-ray images using deep learning. Comput. Struct. Biotechnol. J. 18, 967\u2013972 (2020). https:\/\/doi.org\/10.1016\/j.csbj.2020.04.005","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"6","key":"11_CR2","doi-asserted-by":"publisher","first-page":"482","DOI":"10.3390\/jpm11060482","volume":"11","author":"H Sultan","year":"2021","unstructured":"Sultan, H., Owais, M., Park, C., Mahmood, T., Haider, A., Park, K.R.: Artificial intelligence-based recognition of different types of shoulder implants in X-ray scans based on dense residual ensemble-network for personalized medicine. J. Personalized Med. 11(6), 482 (2021)","journal-title":"J. Personalized Med."},{"key":"11_CR3","unstructured":"Hermena, S., Rednam, M.: Reverse shoulder arthroplasty, 1 October 2022. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; PMID: 34662059, January 2023"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Harrison, A.K., Knudsen, M.L., Braman, J.P.: Hemiarthroplasty and total shoulder arthroplasty conversion to reverse total shoulder arthroplasty. Curr. Rev. Musculoskelet. Med. 13, 501\u2013508 (2020)","DOI":"10.1007\/s12178-020-09649-5"},{"issue":"4","key":"11_CR5","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.jses.2019.10.047","volume":"3","author":"KX Farley","year":"2019","unstructured":"Farley, K.X., Wilson, J.M., Daly, C.A., Gottschalk, M.B., Wagner, E.R.: The incidence of shoulder arthroplasty: rise and future projections compared to hip and knee arthroplasty. JSES Open Access 3(4), 244 (2019)","journal-title":"JSES Open Access"},{"issue":"21","key":"11_CR6","doi-asserted-by":"publisher","first-page":"5123","DOI":"10.3390\/jcm10215123","volume":"10","author":"A Klug","year":"2021","unstructured":"Klug, A., Herrmann, E., Fischer, S., Hoffmann, R., Gramlich, Y.: Projections of primary and revision shoulder arthroplasty until 2040: facing a massive rise in fracture-related procedures. J. Clin. Med. 10(21), 5123 (2021)","journal-title":"J. Clin. Med."},{"issue":"6","key":"11_CR7","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/j.otsr.2020.04.019","volume":"106","author":"G Villatte","year":"2020","unstructured":"Villatte, G., Erivan, R., Barth, J., Bonnevialle, N., Descamps, S., Boisgard, S.: Progression and projection for shoulder surgery in France, 2012\u20132070: epidemiologic study with trend and projection analysis. Orthop. Traumatol. Surg. Res. 106(6), 1067\u20131077 (2020)","journal-title":"Orthop. Traumatol. Surg. Res."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Padegimas, E.M., Maltenfort, M., Lazarus, M.D., Ramsey, M.L., Williams, G.R., Namdari, S.: Future patient demand for shoulder arthroplasty by younger patients: national projections. Clin. Orthopaedics Relat. Res. 473, 1860\u20131867 (2015)","DOI":"10.1007\/s11999-015-4231-z"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s40122-020-00186-0","volume":"9","author":"C Fossati","year":"2020","unstructured":"Fossati, C., Vitale, M., Forin Valvecchi, T., Gualtierotti, R., Randelli, P.S.: Management of painful shoulder arthroplasty: a narrative review. Pain Ther. 9, 427\u2013439 (2020)","journal-title":"Pain Ther."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Borjali, A., Chen, A.F., Muratoglu, O.K., Morid, M.A., Varadarajan, K.M.: Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J. Orthopaedic Res. 38(7), 1465\u20131471 (2020)","DOI":"10.1002\/jor.24617"},{"issue":"6","key":"11_CR11","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","volume":"88","author":"J Olczak","year":"2017","unstructured":"Olczak, J., et al.: Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms\u2014are they on par with humans for diagnosing fractures? Acta Orthop. 88(6), 581\u2013586 (2017)","journal-title":"Acta Orthop."},{"issue":"5","key":"11_CR12","doi-asserted-by":"publisher","first-page":"2327","DOI":"10.1002\/mp.14705","volume":"48","author":"A Borjali","year":"2021","unstructured":"Borjali, A., et al.: Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs. Med. Phys. 48(5), 2327\u20132336 (2021)","journal-title":"Med. Phys."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40729-020-00250-6","volume":"6","author":"T Takahashi","year":"2020","unstructured":"Takahashi, T., Nozaki, K., Gonda, T., Mameno, T., Wada, M., Ikebe, K.: Identification of dental implants using deep learning\u2014pilot study. Int. J. Implant Dent. 6, 1\u20136 (2020)","journal-title":"Int. J. Implant Dent."},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-20132-7","volume":"8","author":"A Tiulpin","year":"2018","unstructured":"Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. 8(1), 1\u201310 (2018)","journal-title":"Sci. Rep."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s00256-020-03463-3","volume":"49","author":"PH Yi","year":"2020","unstructured":"Yi, P.H., et al.: Automated detection and classification of shoulder arthroplasty models using deep learning. Skeletal Radiol. 49, 1623\u20131632 (2020)","journal-title":"Skeletal Radiol."},{"issue":"3","key":"11_CR16","first-page":"447","volume":"56","author":"MT Vo","year":"2022","unstructured":"Vo, M.T., Vo, A.H., Le, T.: A robust framework for shoulder implant X-ray image classification. Data Technol. Appl. 56(3), 447\u2013460 (2022)","journal-title":"Data Technol. Appl."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 117\u2013122. IEEE (2018)","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Khalifa, N.E., Loey, M., Mirjalili, S.: A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif. Intell. Rev. 55, pp. 1\u201327 (2022)","DOI":"10.1007\/s10462-021-10066-4"},{"issue":"6","key":"11_CR19","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2(6), 420 (2021)","journal-title":"SN Comput. Sci."},{"key":"11_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Bansal, M., Kumar, M., Sachdeva, M., Mittal, A.: Transfer learning for image classification using VGG19: Caltech-101 image data set. J. Ambient Intell. Human. Comput. 14, 1\u201312 (2021)","DOI":"10.1007\/s12652-021-03488-z"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Pulmonary image classification based on inception-v3 transfer learning model (2019)","DOI":"10.1109\/ACCESS.2019.2946000"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Sistaninejhad, B., Rasi, H., Nayeri, P.: A review paper about deep learning for medical image analysis. Comput. Math. Methods Med. 2023, 1 (2023)","DOI":"10.1155\/2023\/7091301"},{"key":"11_CR24","unstructured":"Vakili, M., Ghamsari, M., Rezaei, M.: Performance analysis and comparison of machine and deep learning algorithms for IoT data classification. arXiv preprint arXiv:2001.09636 (2020)"},{"key":"11_CR25","doi-asserted-by":"publisher","unstructured":"Ramanathan, A., Christy Bobby, T.: Classification of corpus callosum layer in mid-saggital MRI images using machine learning techniques for autism disorder. In: Modeling, Machine Learning and Astronomy: First International Conference, MMLA 2019, Bangalore, India, 22\u201323 November 2019, Revised Selected Papers, vol. 1, pp. 78\u201391. Springer Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-33-6463-9_7","DOI":"10.1007\/978-981-33-6463-9_7"},{"issue":"5","key":"11_CR26","first-page":"566","volume":"6","author":"A Lydia","year":"2019","unstructured":"Lydia, A., Francis, S.: AdaGrad\u2014an optimizer for stochastic gradient descent. Int. J. Inf. Comput. Sci. 6(5), 566\u2013568 (2019)","journal-title":"Int. J. Inf. Comput. Sci."},{"key":"11_CR27","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR28","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"issue":"3","key":"11_CR29","doi-asserted-by":"publisher","first-page":"346","DOI":"10.3390\/coatings11030346","volume":"11","author":"A Y\u0131lmaz","year":"2021","unstructured":"Y\u0131lmaz, A.: Shoulder implant manufacturer detection by using deep learning: proposed channel selection layer. Coatings 11(3), 346 (2021)","journal-title":"Coatings"},{"key":"11_CR30","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jor.2022.11.004","volume":"35","author":"EA Geng","year":"2023","unstructured":"Geng, E.A., et al.: Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images. J. Orthop. 35, 74\u201378 (2023)","journal-title":"J. Orthop."}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53085-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T08:03:09Z","timestamp":1712304189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53085-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530845","9783031530852"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53085-2_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Derby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rtip2r-conference.org\/2023\/","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":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","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":"62","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":"29% - 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":"2.39","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":"2.79","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)"}}]}}