{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:07:33Z","timestamp":1743059253892,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031543203"},{"type":"electronic","value":"9783031543210"}],"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\/978-3-031-54321-0_10","type":"book-chapter","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T14:02:18Z","timestamp":1708956138000},"page":"141-155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Knee Meniscus Damage Prediction from\u00a0MRI Images with\u00a0Machine Learning and\u00a0Deep Learning Techniques"],"prefix":"10.1007","author":[{"given":"Martin","family":"Kostadinov","sequence":"first","affiliation":[]},{"given":"Petre","family":"Lameski","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Kulakov","sequence":"additional","affiliation":[]},{"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[]},{"given":"Paulo Jorge","family":"Coelho","sequence":"additional","affiliation":[]},{"given":"Eftim","family":"Zdravevski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Corizzo, R., Dauphin, Y., Bellinger, C., Zdravevski, E., Japkowicz, N.: Explainable image analysis for decision support in medical healthcare. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 4667\u20134674 (2021)","DOI":"10.1109\/BigData52589.2021.9671335"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"54574","DOI":"10.1109\/ACCESS.2020.2981315","volume":"8","author":"P Maresova","year":"2020","unstructured":"Maresova, P., et al.: Health-related ICT solutions of smart environments for elderly-systematic review. IEEE Access 8, 54574\u201354600 (2020)","journal-title":"IEEE Access"},{"issue":"17","key":"10_CR3","doi-asserted-by":"publisher","first-page":"5762","DOI":"10.3390\/s21175762","volume":"21","author":"F Ferreira","year":"2021","unstructured":"Ferreira, F., et al.: Experimental study on wound area measurement with mobile devices. Sensors 21(17), 5762 (2021)","journal-title":"Sensors"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1097\/RLI.0000000000000907","volume":"58","author":"B Fritz","year":"2023","unstructured":"Fritz, B., Yi, P., Kijowski, R., Fritz, J.: Radiomics and deep learning for disease detection in musculoskeletal radiology: an overview of novel MRI- and CT-based approaches. Invest. Radiol. 58(1), 3\u201313 (2023)","journal-title":"Invest. Radiol."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Hegde, A., George, R.M., Ranjith, H.: Detection and classification of knee osteoarthritis using texture descriptor algorithms. In: Intelligent Interactive Multimedia Systems for E-Healthcare Applications, pp. 151\u2013166. Apple Academic Press (2022)","DOI":"10.1201\/9781003282112-10"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"635","DOI":"10.2165\/00007256-200636080-00001","volume":"36","author":"C Senter","year":"2006","unstructured":"Senter, C., Hame, S.L.: Biomechanical analysis of tibial torque and knee flexion angle: implications for understanding knee injury. Sports Med. 36, 635\u2013641 (2006)","journal-title":"Sports Med."},{"issue":"10","key":"10_CR7","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1136\/bjsports-2019-100765","volume":"54","author":"T Lien-Iversen","year":"2020","unstructured":"Lien-Iversen, T., Morgan, D.B., Jensen, C., Risberg, M.A., Engebretsen, L., Viberg, B.: Does surgery reduce knee osteoarthritis, meniscal injury and subsequent complications compared with non-surgery after ACL rupture with at least 10 years follow-up? A systematic review and meta-analysis. Br. J. Sports Med. 54(10), 592\u2013598 (2020)","journal-title":"Br. J. Sports Med."},{"issue":"1","key":"10_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1302\/0301-620X.85B1.13956","volume":"85","author":"R Allum","year":"2003","unstructured":"Allum, R.: Complications of arthroscopic reconstruction of the anterior cruciate ligament. J. Bone Joint Surg. 85(1), 12\u201316 (2003)","journal-title":"J. Bone Joint Surg."},{"issue":"1","key":"10_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/S0278-5919(20)30263-5","volume":"14","author":"PA Renstr\u00f6m","year":"1995","unstructured":"Renstr\u00f6m, P.A.: Knee pain in tennis players. Clin. Sports Med. 14(1), 163\u2013175 (1995)","journal-title":"Clin. Sports Med."},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.mad.2019.03.003","volume":"180","author":"MS O\u2019Brien","year":"2019","unstructured":"O\u2019Brien, M.S., McDougall, J.J.: Age and frailty as risk factors for the development of osteoarthritis. Mech. Ageing Dev. 180, 21\u201328 (2019)","journal-title":"Mech. Ageing Dev."},{"issue":"3","key":"10_CR11","doi-asserted-by":"publisher","first-page":"e24","DOI":"10.1097\/JSA.0000000000000329","volume":"29","author":"BG Adams","year":"2021","unstructured":"Adams, B.G., Houston, M.N., Cameron, K.L.: The epidemiology of meniscus injury. Sports Med. Arthrosc. Rev. 29(3), e24\u2013e33 (2021)","journal-title":"Sports Med. Arthrosc. Rev."},{"issue":"3","key":"10_CR12","first-page":"378","volume":"9","author":"R Novriansyah","year":"2022","unstructured":"Novriansyah, R., Kusuma, F.A.: Knee pain due to loose body in the knee joint: a case report in Dr. Kariadi general hospital Semarang. Med. Hospit.: J. Clin. Med. 9(3), 378\u2013382 (2022)","journal-title":"Med. Hospit.: J. Clin. Med."},{"issue":"1","key":"10_CR13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1056\/NEJMcp1903768","volume":"384","author":"L Sharma","year":"2021","unstructured":"Sharma, L.: Osteoarthritis of the knee. N. Engl. J. Med. 384(1), 51\u201359 (2021)","journal-title":"N. Engl. J. Med."},{"issue":"9","key":"10_CR14","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1016\/j.arthro.2011.03.088","volume":"27","author":"ES Paxton","year":"2011","unstructured":"Paxton, E.S., Stock, M.V., Brophy, R.H.: Meniscal repair versus partial meniscectomy: a systematic review comparing reoperation rates and clinical outcomes. Arthrosc.: J. Arthrosc. Relat. Surg. 27(9), 1275\u20131288 (2011)","journal-title":"Arthrosc.: J. Arthrosc. Relat. Surg."},{"issue":"2","key":"10_CR15","doi-asserted-by":"publisher","first-page":"537","DOI":"10.3390\/diagnostics12020537","volume":"12","author":"A Siouras","year":"2022","unstructured":"Siouras, A., et al.: Knee injury detection using deep learning on MRI studies: a systematic review. Diagnostics 12(2), 537 (2022)","journal-title":"Diagnostics"},{"issue":"3","key":"10_CR16","first-page":"180091","volume":"1","author":"F Liu","year":"2019","unstructured":"Liu, F., et al.: Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol.: Artif. Intell. 1(3), 180091 (2019)","journal-title":"Radiol.: Artif. Intell."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Sayegh, E.T., Matzkin, E.: Classifications in brief: the international society of arthroscopy, knee surgery, and orthopaedic sports medicine classification of meniscal tears. Clin. Orthop. Relat. Res.\u00ae 480(1), 39\u201344 (2022)","DOI":"10.1097\/CORR.0000000000001948"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Deep learning-based magnetic resonance imaging image features for diagnosis of anterior cruciate ligament injury. J. Healthc. Eng. 2021 (2021)","DOI":"10.1155\/2021\/4076175"},{"issue":"14","key":"10_CR19","doi-asserted-by":"publisher","first-page":"3906","DOI":"10.3390\/s20143906","volume":"20","author":"B Petrovska","year":"2020","unstructured":"Petrovska, B., Zdravevski, E., Lameski, P., Corizzo, R., \u0160tajduhar, I., Lerga, J.: Deep learning for feature extraction in remote sensing: a case-study of aerial scene classification. Sensors 20(14), 3906 (2020)","journal-title":"Sensors"},{"issue":"4","key":"10_CR20","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.diii.2019.02.007","volume":"100","author":"V Roblot","year":"2019","unstructured":"Roblot, V., et al.: Artificial intelligence to diagnose meniscus tears on MRI. Diagn. Interv. Imaging 100(4), 243\u2013249 (2019)","journal-title":"Diagn. Interv. Imaging"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ejmp.2021.02.010","volume":"83","author":"B Rizk","year":"2021","unstructured":"Rizk, B., et al.: Meniscal lesion detection and characterization in adult knee MRI: a deep learning model approach with external validation. Phys. Med. 83, 64\u201371 (2021)","journal-title":"Phys. Med."},{"issue":"2","key":"10_CR22","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s00256-021-03830-8","volume":"51","author":"B Fritz","year":"2022","unstructured":"Fritz, B., Fritz, J.: Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches. Skeletal Radiol. 51(2), 315\u2013329 (2022)","journal-title":"Skeletal Radiol."},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Javed Awan, M., Mohd Rahim, M.S., Salim, N., Mohammed, M.A., Garcia-Zapirain, B., Abdulkareem, K.H.: Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach. Diagnostics 11(1) (2021)","DOI":"10.3390\/diagnostics11010105"},{"issue":"11","key":"10_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pmed.1002699","volume":"15","author":"N Bien","year":"2018","unstructured":"Bien, N., et al.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLOS Med. 15(11), 1\u201319 (2018)","journal-title":"PLOS Med."},{"issue":"7","key":"10_CR25","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971\u2013987 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-319-16220-1_5","volume-title":"Computer Vision - ECCV 2014 Workshops","author":"J-M Pape","year":"2015","unstructured":"Pape, J.-M., Klukas, C.: 3-D histogram-based segmentation and leaf detection for rosette plants. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8928, pp. 61\u201374. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16220-1_5"},{"issue":"2","key":"10_CR27","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vision"},{"issue":"12","key":"10_CR28","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/0031-3203(91)90143-S","volume":"24","author":"AK Jain","year":"1991","unstructured":"Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recogn. 24(12), 1167\u20131186 (1991)","journal-title":"Pattern Recogn."},{"key":"10_CR29","doi-asserted-by":"publisher","first-page":"100203","DOI":"10.1016\/j.bdr.2021.100203","volume":"25","author":"M Grzegorowski","year":"2021","unstructured":"Grzegorowski, M., Zdravevski, E., Janusz, A., Lameski, P., Apanowicz, C., Slezak, D.: Cost optimization for big data workloads based on dynamic scheduling and cluster-size tuning. Big Data Res. 25, 100203 (2021)","journal-title":"Big Data Res."}],"container-title":["Communications in Computer and Information Science","ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54321-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T14:03:59Z","timestamp":1708956239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54321-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031543203","9783031543210"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54321-0_10","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":"27 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICT Innovations","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on ICT Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ohrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"North Macedonia","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":"24 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ictinnovations2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ictinnovations.org\/","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":"52","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":"17","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":"33% - 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":"3.29","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":"1.63","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}