{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:23:54Z","timestamp":1779222234099,"version":"3.51.4"},"reference-count":123,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"School of Management, Universiti Sains Malaysia","award":["11800"],"award-info":[{"award-number":["11800"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-024-00718-y","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T13:26:24Z","timestamp":1734528384000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Systematic Review of Artificial Intelligence in Orthopaedic Disease Detection: A Taxonomy for Analysis and Trustworthiness Evaluation"],"prefix":"10.1007","volume":"17","author":[{"given":"Thura J.","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chew","family":"Xinying","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alhamzah","family":"Alnoor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khai Wah","family":"Khaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. S.","family":"Albahri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei Lin","family":"Teoh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi Lin","family":"Chong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sajal","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"718_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.jseint.2022.10.004","volume":"7","author":"P Gupta","year":"2023","unstructured":"Gupta, P., Marigi, E.M., Sanchez-Sotelo, J.: Research on artificial intelligence in shoulder and elbow surgery is increasing. JSES Int. 7, 158\u2013161 (2023). https:\/\/doi.org\/10.1016\/j.jseint.2022.10.004","journal-title":"JSES Int."},{"key":"718_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20054521","author":"DS Overstreet","year":"2023","unstructured":"Overstreet, D.S., Strath, L.J., Jordan, M., Jordan, I.A., Hobson, J.M., Owens, M.A., Williams, A.C., Edwards, R.R., Meints, S.M.: A brief overview: sex differences in prevalent chronic musculoskeletal conditions. Int. J. Environ. Res. Public Health (2023). https:\/\/doi.org\/10.3390\/ijerph20054521","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"718_CR3","doi-asserted-by":"publisher","first-page":"110770","DOI":"10.1016\/j.celrep.2022.110770","volume":"39","author":"H Guo","year":"2022","unstructured":"Guo, H., Gao, Y., Li, T., Li, T., Lu, Y., Zheng, L., Liu, Y., Yang, T., Luo, F., Song, S., Wang, W., Yang, X., Nguyen, H.C., Zhang, H., Huang, A., Jin, A., Yang, H., Rao, Z., Ji, X.: Structures of Omicron spike complexes and implications for neutralizing antibody development. Cell Rep. 39, 110770 (2022). https:\/\/doi.org\/10.1016\/j.celrep.2022.110770","journal-title":"Cell Rep."},{"key":"718_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbspin.2022.105493","volume":"90","author":"V Boussona","year":"2023","unstructured":"Boussona, V., Benoista, N., Guetata, P., Attane, G., Salvatc, C., Perronnea, L., Bousson, V., Benoist, N., Guetat, P., Attan\u00e9, G., Salvat, C., Perronne, L., Boussona, V., Benoista, N., Guetata, P., Attane, G., Salvatc, C., Perronnea, L.: Application of artificial intelligence to imaging interpretations in the musculoskeletal area: where are we? Where are we going? Jt. Bone Spine 90, 105493 (2023). https:\/\/doi.org\/10.1016\/j.jbspin.2022.105493","journal-title":"Jt. Bone Spine"},{"key":"718_CR5","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.radi.2021.01.008","volume":"27","author":"BO Botwe","year":"2021","unstructured":"Botwe, B.O., Akudjedu, T.N., Antwi, W.K., Rockson, P., Mkoloma, S.S., Balogun, E.O., Elshami, W., Bwambale, J., Barare, C., Mdletshe, S., Yao, B., Arkoh, S.: The integration of artificial intelligence in medical imaging practice: perspectives of African radiographers. Radiography 27, 861\u2013866 (2021). https:\/\/doi.org\/10.1016\/j.radi.2021.01.008","journal-title":"Radiography"},{"key":"718_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.09.004","author":"R Kumar","year":"2021","unstructured":"Kumar, R., Sharma, R.: Leveraging blockchain for ensuring trust in IoT: a survey. J. King Saud Univ. Comput. Inf. Sci. (2021). https:\/\/doi.org\/10.1016\/j.jksuci.2021.09.004","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"718_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-024-00615-4","volume-title":"Knee osteoporosis diagnosis based on deep learning","author":"AM Sarhan","year":"2024","unstructured":"Sarhan, A.M., Gobara, M., Yasser, S., Elsayed, Z., Sherif, G., Moataz, N., Yasir, Y., Moustafa, E., Ibrahim, S., Ali, H.A.: Knee osteoporosis diagnosis based on deep learning. Springer, Netherlands (2024). https:\/\/doi.org\/10.1007\/s44196-024-00615-4"},{"key":"718_CR8","doi-asserted-by":"publisher","first-page":"123610","DOI":"10.1016\/j.eswa.2024.123610","volume":"249","author":"KM Chang","year":"2024","unstructured":"Chang, K.M., Chang, T.Y., Cheng-Yuan Ku, C., Chiu, C.W., Ter Chang, C.: Sharing decision-making in knee osteoarthritis using the AHP-FMCGP method. Expert Syst. Appl. 249, 123610 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.123610","journal-title":"Expert Syst. Appl."},{"key":"718_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102208","volume":"105","author":"ASS Albahri","year":"2023","unstructured":"Albahri, A.S.S., Hamid, R.A., Abdulnabi, A.R., Albahri, O.S.S., Alamoodi, A.H.H., Deveci, M., Pedrycz, W., Alzubaidi, L., Santamar\u00eda, J., Gu, Y.: Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in vehicular ad-hoc networks. Inf. Fusion 105, 102208 (2023). https:\/\/doi.org\/10.1016\/j.inffus.2023.102208","journal-title":"Inf. Fusion"},{"key":"718_CR10","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s44196-024-00543-3","volume":"17","author":"GG Shayea","year":"2024","unstructured":"Shayea, G.G., Zabil, M.H.M., Albahri, A.S., Joudar, S.S., Hamid, R.A., Albahri, O.S., Alamoodi, A.H., Zahid, I.A., Sharaf, I.M.: Fuzzy evaluation and benchmarking framework for robust machine learning model in real-time autism triage applications. Int. J. Comput. Intell. Syst. 17, 151 (2024). https:\/\/doi.org\/10.1007\/s44196-024-00543-3","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"718_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s40634-023-00683-z","author":"B Zsidai","year":"2023","unstructured":"Zsidai, B., Hilkert, A.S., Kaarre, J., Narup, E., Senorski, E.H., Grassi, A., Ley, C., Longo, U.G., Herbst, E., Hirschmann, M.T., Kopf, S., Seil, R., Tischer, T., Samuelsson, K., Feldt, R.: A practical guide to the implementation of AI in orthopaedic research \u2013 part 1: opportunities in clinical application and overcoming existing challenges. J. Exp. Orthop. (2023). https:\/\/doi.org\/10.1186\/s40634-023-00683-z","journal-title":"J. Exp. Orthop."},{"key":"718_CR12","doi-asserted-by":"publisher","first-page":"39757","DOI":"10.1109\/ACCESS.2021.3062493","volume":"9","author":"MR Karim","year":"2021","unstructured":"Karim, M.R., Jiao, J., Dohmen, T., Cochez, M., Beyan, O., Rebholz-Schuhmann, D., Decker, S.: DeepKneeExplainer: explainable knee osteoarthritis diagnosis from radiographs and magnetic resonance imaging. IEEE Access 9, 39757\u201339780 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3062493","journal-title":"IEEE Access"},{"key":"718_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123066","volume":"246","author":"MA Alsalem","year":"2024","unstructured":"Alsalem, M.A., Alamoodi, A.H., Albahri, O.S., Albahri, A.S., Mart\u00ednez, L., Yera, R., Duhaim, A.M., Sharaf, I.M.: Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach. Expert Syst. Appl. 246, 123066 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.123066","journal-title":"Expert Syst. Appl."},{"key":"718_CR14","doi-asserted-by":"publisher","first-page":"1674","DOI":"10.1017\/dmp.2021.125","volume":"16","author":"S Lu","year":"2022","unstructured":"Lu, S., Christie, G.A., Nguyen, T.T., Freeman, J.D., Hsu, E.B.: Applications of artificial intelligence and machine learning in disasters and public health emergencies. Disaster Med. Public Health Prep. 16, 1674\u20131681 (2022). https:\/\/doi.org\/10.1017\/dmp.2021.125","journal-title":"Disaster Med. Public Health Prep."},{"key":"718_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-021-01790-z","volume":"46","author":"E Crigger","year":"2022","unstructured":"Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., Irons, M.: Trustworthy augmented intelligence in health care. J. Med. Syst. 46, 1\u201311 (2022). https:\/\/doi.org\/10.1007\/s10916-021-01790-z","journal-title":"J. Med. Syst."},{"key":"718_CR16","doi-asserted-by":"publisher","DOI":"10.1142\/S0219622024500019","author":"AS Albahri","year":"2024","unstructured":"Albahri, A.S., Jassim, M.M., Alzubaidi, L., Hamid, R.A., Ahmed, M.A., Al-Qaysi, Z.T., Albahri, O.S., Alamoodi, A.H., Alqaysi, M.E., Mohammed, T.J., Kou, G., Alotaibi, F.S., Sharaf, I.M.: A trustworthy and explainable framework for benchmarking hybrid deep learning models based on chest x-ray analysis in CAD systems. Int. J. Inf. Technol. Decis. Mak. (2024). https:\/\/doi.org\/10.1142\/S0219622024500019","journal-title":"Int. J. Inf. Technol. Decis. Mak."},{"key":"718_CR17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.inffus.2021.10.007","volume":"79","author":"A Holzinger","year":"2022","unstructured":"Holzinger, A., Dehmer, M., Emmert-Streib, F., Cucchiara, R., Augenstein, I., Del Ser, J., Samek, W., Jurisica, I., D\u00edaz-Rodr\u00edguez, N.: Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Inf. Fusion 79, 263\u2013278 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2021.10.007","journal-title":"Inf. Fusion"},{"key":"718_CR18","doi-asserted-by":"publisher","first-page":"102935","DOI":"10.1016\/j.artmed.2024.102935","volume":"155","author":"L Alzubaidi","year":"2024","unstructured":"Alzubaidi, L., Dulaimi, K.A.L., Salhi, A., Alammar, Z., Fadhel, M.A., Albahri, A.S., Alamoodi, A.H., Albahri, O.S., Hasan, A.F., Bai, J., Gilliland, L., Peng, J., Branni, M., Shuker, T., Cutbush, K., Santamar\u00eda, J., Moreira, C., Ouyang, C., Duan, Y., Manoufali, M., Jomaa, M., Gupta, A., Abbosh, A., Gu, Y.: Comprehensive review of deep learning in orthopaedics: applications, challenges, trustworthiness, and fusion. Artif. Intell. Med. 155, 102935 (2024). https:\/\/doi.org\/10.1016\/j.artmed.2024.102935","journal-title":"Artif. Intell. Med."},{"key":"718_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103655","volume":"113","author":"AF Markus","year":"2021","unstructured":"Markus, A.F., Kors, J.A., Rijnbeek, P.R.: The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J. Biomed. Inform. 113, 103655 (2021). https:\/\/doi.org\/10.1016\/j.jbi.2020.103655","journal-title":"J. Biomed. Inform."},{"key":"718_CR20","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","volume":"96","author":"AS Albahri","year":"2023","unstructured":"Albahri, A.S., Duhaim, A.M., Fadhel, M.A., Alnoor, A., Baqer, N.S., Alzubaidi, L., Albahri, O.S., Alamoodi, A.H., Bai, J., Salhi, A., Santamar\u00eda, J., Ouyang, C., Gupta, A., Gu, Y., Deveci, M.: A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion. Inf. Fusion 96, 156\u2013191 (2023). https:\/\/doi.org\/10.1016\/j.inffus.2023.03.008","journal-title":"Inf. Fusion"},{"key":"718_CR21","doi-asserted-by":"publisher","first-page":"904","DOI":"10.3390\/ai4040046","volume":"4","author":"E Hohma","year":"2023","unstructured":"Hohma, E., L\u00fctge, C.: From trustworthy principles to a trustworthy development process: the need and elements of trusted development of AI systems. AI. 4, 904\u2013925 (2023). https:\/\/doi.org\/10.3390\/ai4040046","journal-title":"AI."},{"key":"718_CR22","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.1007\/s11948-019-00165-5","volume":"26","author":"J Morley","year":"2020","unstructured":"Morley, J., Floridi, L., Kinsey, L., Elhalal, A.: From what to how: an initial review of publicly available ai ethics tools, methods and research to translate principles into practices. Sci. Eng. Ethics 26, 2141\u20132168 (2020). https:\/\/doi.org\/10.1007\/s11948-019-00165-5","journal-title":"Sci. Eng. Ethics"},{"key":"718_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/app12020681","author":"J Lee","year":"2022","unstructured":"Lee, J., Chung, S.W.: Deep Learning for orthopedic disease based on medical image analysis: present and future. Appl. Sci. (2022). https:\/\/doi.org\/10.3390\/app12020681","journal-title":"Appl. Sci."},{"key":"718_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-024-00422-8","author":"S Gitto","year":"2024","unstructured":"Gitto, S., Serpi, F., Albano, D., Risoleo, G., Fusco, S., Messina, C., Sconfienza, L.M.: AI applications in musculoskeletal imaging: a narrative review. Eur. Radiol. Exp. (2024). https:\/\/doi.org\/10.1186\/s41747-024-00422-8","journal-title":"Eur. Radiol. Exp."},{"key":"718_CR25","doi-asserted-by":"publisher","DOI":"10.1051\/sicotj\/2023018","author":"S Sharma","year":"2023","unstructured":"Sharma, S.: Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential. Sicot-J (2023). https:\/\/doi.org\/10.1051\/sicotj\/2023018","journal-title":"Sicot-J"},{"key":"718_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0260471","volume":"16","author":"SJ Federer","year":"2021","unstructured":"Federer, S.J., Jones, G.G.: Artificial intelligence in orthopaedics: a scoping review. PLoS ONE 16, 1\u201311 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0260471","journal-title":"PLoS ONE"},{"key":"718_CR27","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.ijsu.2010.02.007","volume":"8","author":"D Moher","year":"2010","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int. J. Surg. 8, 336\u2013341 (2010). https:\/\/doi.org\/10.1016\/j.ijsu.2010.02.007","journal-title":"Int. J. Surg."},{"key":"718_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111859","volume":"163","author":"AA Magabaleh","year":"2024","unstructured":"Magabaleh, A.A., Ghraibeh, L.L., Audeh, A.Y., Albahri, A.S., Deveci, M., Antucheviciene, J.: Systematic review of software engineering uses of multi-criteria decision-making methods: trends, bibliographic analysis, challenges, recommendations, and future directions. Appl. Soft Comput. 163, 111859 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2024.111859","journal-title":"Appl. Soft Comput."},{"key":"718_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109409","volume":"118","author":"AS Albahri","year":"2024","unstructured":"Albahri, A.S., Khaleel, Y.L., Habeeb, M.A., Ismael, R.D., Hameed, Q.A., Deveci, M., Homod, R.Z., Albahri, O.S., Alamoodi, A.H., Alzubaidi, L.: A systematic review of trustworthy artificial intelligence applications in natural disasters. Comput. Electr. Eng. 118, 109409 (2024). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109409","journal-title":"Comput. Electr. Eng."},{"key":"718_CR30","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/s10462-024-10881-5","volume":"57","author":"MA Fadhel","year":"2024","unstructured":"Fadhel, M.A., Duhaim, A.M., Albahri, A.S., Al-Qaysi, Z.T., Aktham, M.A., Chyad, M.A., Abd-Alaziz, W., Albahri, O.S., Alamoodi, A.H., Alzubaidi, L., Gupta, A., Gu, Y.: Navigating the metaverse: unraveling the impact of artificial intelligence\u2014a comprehensive review and gap analysis. Artif. Intell. Rev. 57, 264 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10881-5","journal-title":"Artif. Intell. Rev."},{"key":"718_CR31","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12955","author":"N Kour","year":"2022","unstructured":"Kour, N., Gupta, S., Arora, S.: A vision-based clinical analysis for classification of knee osteoarthritis, Parkinson\u2019s disease and normal gait with severity based on k-nearest neighbour. Expert. Syst. (2022). https:\/\/doi.org\/10.1111\/exsy.12955","journal-title":"Expert. Syst."},{"key":"718_CR32","doi-asserted-by":"publisher","first-page":"68870","DOI":"10.1109\/ACCESS.2024.3400987","volume":"12","author":"R Ahmed","year":"2024","unstructured":"Ahmed, R., Imran, A.S.: Knee osteoarthritis analysis using deep learning and XAI on X-rays. IEEE Access 12, 68870\u201368879 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3400987","journal-title":"IEEE Access"},{"key":"718_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines10112714","author":"M Obayya","year":"2022","unstructured":"Obayya, M., Alamgeer, M., Alzahrani, J.S., Alabdan, R., Al-Wesabi, F.N., Mohamed, A., Alsaid Hassan, M.I.: Artificial intelligence driven biomedical image classification for robust rheumatoid arthritis classification. Biomedicines (2022). https:\/\/doi.org\/10.3390\/biomedicines10112714","journal-title":"Biomedicines"},{"key":"718_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13143616","author":"V-H Le","year":"2021","unstructured":"Le, V.-H., Kha, Q.-H., Hung, T.N.K., Le, N.Q.K.: Risk score generated from CT-based radiomics signatures for overall survival prediction in non-small cell lung cancer. Cancers Basel (2021). https:\/\/doi.org\/10.3390\/cancers13143616","journal-title":"Cancers Basel"},{"key":"718_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.suronc.2022.101782","volume":"42","author":"CL Li Brizzi","year":"2022","unstructured":"Li Brizzi, C.L., Rao, S.S., Wang, K.Y., Levin, A.S., Morris, C.D.: Survey of sarcoma surgery principles among orthopaedic oncologists. Surg. Oncol. 42, 101782 (2022). https:\/\/doi.org\/10.1016\/j.suronc.2022.101782","journal-title":"Surg. Oncol."},{"key":"718_CR36","doi-asserted-by":"publisher","first-page":"4371","DOI":"10.21873\/anticanres.15937","volume":"42","author":"S Consalvo","year":"2022","unstructured":"Consalvo, S., Hinterwimmer, F., Neumann, J., Steinborn, M., Salzmann, M., Seidl, F., Lenze, U., Knebel, C., Rueckert, D., Burgkart, R.H.H.: Two-phase deep learning algorithm for detection and differentiation of ewing sarcoma and acute osteomyelitis in paediatric radiographs. Anticancer Res 42, 4371\u20134380 (2022). https:\/\/doi.org\/10.21873\/anticanres.15937","journal-title":"Anticancer Res"},{"key":"718_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103407","author":"S Gitto","year":"2021","unstructured":"Gitto, S., Cuocolo, R., Annovazzi, A., Anelli, V., Acquasanta, M., Cincotta, A., Albano, D., Chianca, V., Ferraresi, V., Messina, C., Zoccali, C., Armiraglio, E., Parafioriti, A., Sciuto, R., Luzzati, A., Biagini, R., Imbriaco, M., Sconfienza, L.M.: CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas. EBioMedicine (2021). https:\/\/doi.org\/10.1016\/j.ebiom.2021.103407","journal-title":"EBioMedicine"},{"key":"718_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14246066","author":"T Vaiyapuri","year":"2022","unstructured":"Vaiyapuri, T., Jothi, A., Narayanasamy, K., Kamatchi, K., Kadry, S., Kim, J.: Design of a honey badger optimization algorithm with a deep transfer learning-based osteosarcoma classification model. Cancers (Basel). (2022). https:\/\/doi.org\/10.3390\/cancers14246066","journal-title":"Cancers (Basel)."},{"key":"718_CR39","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s00256-021-03820-w","volume":"51","author":"MD Li","year":"2022","unstructured":"Li, M.D., Ahmed, S.R., Choy, E., Lozano-Calderon, S.A., Kalpathy-Cramer, J., Chang, C.Y.: Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol. 51, 245\u2013256 (2022). https:\/\/doi.org\/10.1007\/s00256-021-03820-w","journal-title":"Skeletal Radiol."},{"key":"718_CR40","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s11548-019-01933-1","volume":"14","author":"SD Mehta","year":"2019","unstructured":"Mehta, S.D., Sebro, R.: Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies. Int. J. Comput. Assist. Radiol. Surg. 14, 903\u2013909 (2019). https:\/\/doi.org\/10.1007\/s11548-019-01933-1","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"718_CR41","doi-asserted-by":"publisher","unstructured":"Hajianfar, G., Sabouri, M., Bagheri, S., Salimi, Y., Oveisi, M., Shiri, I., Zaidi, H.: Dual input scintigraphy image-based fused deep neural networks for bone abnormalities detection and differentiation. In: 2021 IEEE Nucl. Sci. Symp. Med. Imaging Conf., 2021: pp. 1\u20133. https:\/\/doi.org\/10.1109\/NSS\/MIC44867.2021.9875765","DOI":"10.1109\/NSS\/MIC44867.2021.9875765"},{"key":"718_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.771092","author":"S Albaradei","year":"2021","unstructured":"Albaradei, S., Uludag, M., Thafar, M.A., Gojobori, T., Essack, M., Gao, X.: Predicting bone metastasis using gene expression-based machine learning models. Front. Genet. (2021). https:\/\/doi.org\/10.3389\/fgene.2021.771092","journal-title":"Front. Genet."},{"key":"718_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74135-4","author":"Z Zhao","year":"2020","unstructured":"Zhao, Z., Pi, Y., Jiang, L., Xiang, Y., Wei, J., Yang, P., Zhang, W., Zhong, X., Zhou, K., Li, Y., Li, L., Yi, Z., Cai, H.: Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci. Rep. (2020). https:\/\/doi.org\/10.1038\/s41598-020-74135-4","journal-title":"Sci. Rep."},{"key":"718_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7433186","author":"A Sharma","year":"2021","unstructured":"Sharma, A., Yadav, D.P., Garg, H., Kumar, M., Sharma, B., Koundal, D.: Bone cancer detection using feature extraction based machine learning model. Comput. Math. Methods Med. (2021). https:\/\/doi.org\/10.1155\/2021\/7433186","journal-title":"Comput. Math. Methods Med."},{"key":"718_CR45","doi-asserted-by":"publisher","unstructured":"J.J.B. Jayachandran, S. Ambigapathy, P. Abirami, K. Ishwaryalakshmi, X-ray image analysis in identification of bone cancer using laws features and machine learning model. In: 2022 Int. Conf. Data Sci. Agents Artif. Intell., 2022: pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICDSAAI55433.2022.10028844.","DOI":"10.1109\/ICDSAAI55433.2022.10028844"},{"key":"718_CR46","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1515\/cdbme-2022-1019","volume":"8","author":"M Bloier","year":"2022","unstructured":"Bloier, M., Hinterwimmer, F., Breden, S., Consalvo, S., Neumann, J., Wilhelm, N., von Eisenhart-Rothe, R., Rueckert, D., Burgkart, R.: Detection and segmentation of heterogeneous bone tumours in limited radiographs. Curr. Dir. Biomed. Eng. 8, 69\u201372 (2022). https:\/\/doi.org\/10.1515\/cdbme-2022-1019","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"718_CR47","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1002\/jmri.28025","volume":"56","author":"K Zhao","year":"2022","unstructured":"Zhao, K., Zhang, M., Xie, Z., Yan, X., Wu, S., Liao, P., Lu, H., Shen, W., Fu, C., Cui, H., Fang, Q., Mei, J.: Deep learning assisted diagnosis of musculoskeletal tumors based on contrast-enhanced magnetic resonance imaging. J. Magn. Reson. Imaging 56, 99\u2013107 (2022). https:\/\/doi.org\/10.1002\/jmri.28025","journal-title":"J. Magn. Reson. Imaging"},{"key":"718_CR48","doi-asserted-by":"publisher","first-page":"102141","DOI":"10.1016\/j.compmedimag.2022.102141","volume":"102","author":"Z Xu","year":"2022","unstructured":"Xu, Z., Niu, K., Tang, S., Song, T., Rong, Y., Guo, W., He, Z.: Bone tumor necrosis rate detection in few-shot X-rays based on deep learning. Comput. Med. Imaging Graph. 102, 102141 (2022). https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102141","journal-title":"Comput. Med. Imaging Graph."},{"key":"718_CR49","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1148\/radiol.2021204531","volume":"301","author":"CE von Schacky","year":"2021","unstructured":"von Schacky, C.E., Wilhelm, N.J., Sch\u00e4fer, V.S., Leonhardt, Y., Gassert, F.G., Foreman, S.C., Gassert, F.T., Jung, M., Jungmann, P.M., Russe, M.F., Mogler, C., Knebel, C., von Eisenhart-Rothe, R., Makowski, M.R., Woertler, K., Burgkart, R., Gersing, A.S.: Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology 301, 398\u2013406 (2021). https:\/\/doi.org\/10.1148\/radiol.2021204531","journal-title":"Radiology"},{"key":"718_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11040691","author":"N-T Do","year":"2021","unstructured":"Do, N.-T., Jung, S.-T., Yang, H.-J., Kim, S.-H.: Multi-Level seg-unet model with global and patch-based X-ray images for knee bone tumor detection. Diagnostics (Basel, Switzerland) (2021). https:\/\/doi.org\/10.3390\/diagnostics11040691","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"718_CR51","doi-asserted-by":"publisher","first-page":"e0264140","DOI":"10.1371\/journal.pone.0264140","volume":"17","author":"C-W Park","year":"2022","unstructured":"Park, C.-W., Oh, S.-J., Kim, K.-S., Jang, M.-C., Kim, I.S., Lee, Y.-K., Chung, M.J., Cho, B.H., Seo, S.-W.: Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation. PLoS ONE 17, e0264140 (2022). https:\/\/doi.org\/10.1371\/journal.pone.0264140","journal-title":"PLoS ONE"},{"key":"718_CR52","doi-asserted-by":"publisher","first-page":"3175","DOI":"10.1007\/s12011-022-03417-x","volume":"201","author":"J Xu","year":"2023","unstructured":"Xu, J., Wang, J., Zhao, H.: The prevalence of kashin-beck disease in China: a systematic review and meta-analysis. Biol. Trace Elem. Res. 201, 3175\u20133184 (2023). https:\/\/doi.org\/10.1007\/s12011-022-03417-x","journal-title":"Biol. Trace Elem. Res."},{"key":"718_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105919","author":"J Dang","year":"2021","unstructured":"Dang, J., Li, H., Niu, K., Xu, Z., Lin, J., He, Z.: Kashin-beck disease diagnosis based on deep learning from hand X-ray images. Comput. Methods Programs Biomed. (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105919","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR54","doi-asserted-by":"publisher","first-page":"3944","DOI":"10.1109\/TMI.2020.3008382","volume":"39","author":"C Liu","year":"2020","unstructured":"Liu, C., Xie, H., Zhang, S., Mao, Z., Sun, J., Zhang, Y.: Misshapen Pelvis landmark detection with local-global feature learning for diagnosing developmental dysplasia of the hip. IEEE Trans. Med. Imaging 39, 3944\u20133954 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3008382","journal-title":"IEEE Trans. Med. Imaging"},{"key":"718_CR55","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2021.785480","volume":"9","author":"W Xu","year":"2022","unstructured":"Xu, W., Shu, L., Gong, P., Huang, C., Xu, J., Zhao, J., Shu, Q., Zhu, M., Qi, G., Zhao, G., Yu, G.: A deep-learning aided diagnostic system in assessing developmental dysplasia of the hip on pediatric pelvic radiographs. Front. Pediatr. 9, 785480 (2022). https:\/\/doi.org\/10.3389\/fped.2021.785480","journal-title":"Front. Pediatr."},{"key":"718_CR56","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12112597","author":"J Jensen","year":"2022","unstructured":"Jensen, J., Graumann, O., Overgaard, S., Gerke, O., Lundemann, M., Haubro, M.H., Varnum, C., Bak, L., Rasmussen, J., Olsen, L.B., Rasmussen, B.S.B.: A deep learning algorithm for radiographic measurements of the hip in adults-a reliability and agreement study. Diagnostics (Basel, Switzerland) (2022). https:\/\/doi.org\/10.3390\/diagnostics12112597","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"718_CR57","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s00264-022-05311-6","volume":"46","author":"P Hernigou","year":"2022","unstructured":"Hernigou, P., Safar, A., Hernigou, J., Ferre, B.: Subtalar axis determined by combining digital twins and artificial intelligence: influence of the orientation of this axis for hindfoot compensation of varus and valgus knees. Int. Orthop. 46, 999\u20131007 (2022). https:\/\/doi.org\/10.1007\/s00264-022-05311-6","journal-title":"Int. Orthop."},{"key":"718_CR58","doi-asserted-by":"publisher","first-page":"106080","DOI":"10.1016\/j.cmpb.2021.106080","volume":"205","author":"A Tack","year":"2021","unstructured":"Tack, A., Preim, B., Zachow, S.: Fully automated assessment of knee alignment from full-leg X-Rays employing a \u201cYOLOv4 And Resnet Landmark regression Algorithm\u201d (YARLA): data from the osteoarthritis initiative. Comput. Methods Programs Biomed. 205, 106080 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106080","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110414","volume":"154","author":"YM Van der Britt Kolk","year":"2022","unstructured":"Van der Britt Kolk, Y.M., Jorik Slotman, D.J., Nijholt, I.M., van Osch, J.A.C., Snoeijink, T.J., Podlogar, M., van Hasselt, B.A.A.M., Boelhouwers, H.J., van Stralen, M., Seevinck, P.R., Schep, N.W.L., Maas, M., Boomsma, M.F.: Bone visualization of the cervical spine with deep learning-based synthetic CT compared to conventional CT: a single-center noninferiority study on image quality. Eur. J. Radiol. 154, 110414 (2022). https:\/\/doi.org\/10.1016\/j.ejrad.2022.110414","journal-title":"Eur. J. Radiol."},{"key":"718_CR60","doi-asserted-by":"publisher","unstructured":"Chen, Q., Liao, R., Shalaginov, M.Y., Zeng, T.H.: Scoliosis detection with convolutional neural networks. In: 2022 IEEE Int. Conf. Bioinforma. Biomed., 2022: pp. 3785\u20133787. https:\/\/doi.org\/10.1109\/BIBM55620.2022.9995579","DOI":"10.1109\/BIBM55620.2022.9995579"},{"key":"718_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/3796202","volume":"2022","author":"P Chen","year":"2022","unstructured":"Chen, P., Zhou, Z., Yu, H., Chen, K., Yang, Y.: Computerized-assisted scoliosis diagnosis based on faster R-CNN and ResNet for the classification of spine X-ray images. Comput. Math. Methods Med. 2022, 1\u201313 (2022). https:\/\/doi.org\/10.1155\/2022\/3796202","journal-title":"Comput. Math. Methods Med."},{"key":"718_CR62","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11832-012-0457-4","volume":"7","author":"MR Konieczny","year":"2013","unstructured":"Konieczny, M.R., Senyurt, H., Krauspe, R.: Epidemiology of adolescent idiopathic scoliosis. J. Child. Orthop. 7, 3\u20139 (2013). https:\/\/doi.org\/10.1007\/s11832-012-0457-4","journal-title":"J. Child. Orthop."},{"key":"718_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102371","volume":"65","author":"TP Nguyen","year":"2021","unstructured":"Nguyen, T.P., Chae, D.-S., Park, S.-J., Kang, K.-Y., Yoon, J.: Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis. Biomed. Signal Process. Control 65, 102371 (2021). https:\/\/doi.org\/10.1016\/j.bspc.2020.102371","journal-title":"Biomed. Signal Process. Control"},{"key":"718_CR64","doi-asserted-by":"publisher","unstructured":"Makhdoomi, N.A., Gunawan, T.S., Idris, N.H., Khalifa, O.O., Karupiah, R.K, Bramantoro, A., Abdul Rahman, F.D., Zakaria Z.: Development of scoliotic spine severity detection using deep learning algorithms. In: 2022 IEEE 12th Annu. Comput. Commun. Work. Conf. CCWC 2022, 2022: pp. 574\u2013579. https:\/\/doi.org\/10.1109\/CCWC54503.2022.9720906","DOI":"10.1109\/CCWC54503.2022.9720906"},{"key":"718_CR65","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-19914-x","author":"T Fujimori","year":"2022","unstructured":"Fujimori, T., Suzuki, Y., Takenaka, S., Kita, K., Kanie, Y., Kaito, T., Ukon, Y., Watabe, T., Nakajima, N., Kido, S., Okada, S.: Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs. Sci. Rep. (2022). https:\/\/doi.org\/10.1038\/s41598-022-19914-x","journal-title":"Sci. Rep."},{"key":"718_CR66","doi-asserted-by":"publisher","unstructured":"Yang, F., Ding, B.: Computer aided fracture diagnosis based on integrated learning. In: 2020 IEEE 3rd Int. Conf. Inf. Syst. Comput. Aided Educ., 2020: pp. 523\u2013527. https:\/\/doi.org\/10.1109\/ICISCAE51034.2020.9236917","DOI":"10.1109\/ICISCAE51034.2020.9236917"},{"key":"718_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106304","volume":"208","author":"AW Olthof","year":"2021","unstructured":"Olthof, A.W., Shouche, P., Fennema, E.M., IJpma, F.F.A., Koolstra, R.H.C., Stirler, V.M.A., van Ooijen, P.M.A., Cornelissen, L.J.: Machine learning based natural language processing of radiology reports in orthopaedic trauma. Comput. Methods Programs Biomed. 208, 106304 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106304","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR68","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/17453674.2021.1891512","volume":"92","author":"G Zdolsek","year":"2021","unstructured":"Zdolsek, G., Chen, Y., Bogl, H.-P., Wang, C., Woisetschlager, M., Schilcher, J.: Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures. ACTA Orthop. 92, 394\u2013400 (2021). https:\/\/doi.org\/10.1080\/17453674.2021.1891512","journal-title":"ACTA Orthop."},{"key":"718_CR69","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-70660-4","author":"C Lee","year":"2020","unstructured":"Lee, C., Jang, J., Lee, S., Kim, Y.S., Jo, H.J., Kim, Y.: Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network. Sci. Rep. (2020). https:\/\/doi.org\/10.1038\/s41598-020-70660-4","journal-title":"Sci. Rep."},{"key":"718_CR70","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2022.927926","volume":"10","author":"P Liu","year":"2022","unstructured":"Liu, P., Lu, L., Chen, Y., Huo, T., Xue, M., Wang, H., Fang, Y., Xie, Y., Xie, M., Ye, Z.: Artificial intelligence to detect the femoral intertrochanteric fracture: the arrival of the intelligent-medicine era. Front. Bioeng. Biotechnol. 10, 927926 (2022). https:\/\/doi.org\/10.3389\/fbioe.2022.927926","journal-title":"Front. Bioeng. Biotechnol."},{"key":"718_CR71","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s12553-021-00543-9","volume":"11","author":"K Acici","year":"2021","unstructured":"Acici, K., Sumer, E., Beyaz, S.: Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images. Health Technol. (Berl) 11, 643\u2013653 (2021). https:\/\/doi.org\/10.1007\/s12553-021-00543-9","journal-title":"Health Technol. (Berl)"},{"key":"718_CR72","doi-asserted-by":"publisher","DOI":"10.1007\/s00068-022-02136-1","author":"J Prijs","year":"2022","unstructured":"Prijs, J., Liao, Z., To, M.-S., Verjans, J., Jutte, P.C., Stirler, V., Olczak, J., Gordon, M., Guss, D., DiGiovanni, C.W., Jaarsma, R.R.L., IJpma, F.F.A., Doornberg, J.N., Aksakal, K., Barvelink, B., Beuker, B., Bultra, A.E., Oliviera, L.C., Colaris, J., de Klerk, H., Duckworth, A., Ten Duis, K., Fennema, E., Harbers, J., Hendrickx, R., Heng, M., Hoeksema, S., Hogervorst, M., Jadav, B., Jiang, J., Karhade, A., Kerkhoffs, G., Kuipers, J., Laane, C., Langerhuizen, D., Lubberts, B., Mallee, W., Mhmud, H., El Moumni, M., Nieboer, P., Nijhuis, K.O., van Ooijen, P., Oosterhoff, J., Rawat, J., Ring, D., Schilstra, S., Schwab, J., Sprague, S., Stufkens, S., Tijdens, E., van der Bekerom, M., van der Vet, P., de Vries, J.-P., Wendt, K., Wijffels, M., Worsley, D., the M.L. Consortium: Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN). Eur. J. Trauma Emerg. Surg. (2022). https:\/\/doi.org\/10.1007\/s00068-022-02136-1","journal-title":"Eur. J. Trauma Emerg. Surg."},{"key":"718_CR73","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1080\/17453674.2020.1837420","volume":"92","author":"J Olczak","year":"2020","unstructured":"Olczak, J., Emilson, F., Razavian, A., Antonsson, T., Stark, A., Gordon, M.: Ankle fracture classification using deep learning: automating detailed AO foundation\/orthopedic trauma association (AO\/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. ACTA Orthop. 92, 102\u2013108 (2020). https:\/\/doi.org\/10.1080\/17453674.2020.1837420","journal-title":"ACTA Orthop."},{"key":"718_CR74","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1016\/j.fas.2022.05.005","volume":"28","author":"S Ashkani-Esfahani","year":"2022","unstructured":"Ashkani-Esfahani, S., Mojahed Yazdi, R., Bhimani, R., Kerkhoffs, G.M., Maas, M., DiGiovanni, C.W., Lubberts, B., Guss, D.: Detection of ankle fractures using deep learning algorithms. Foot Ankle Surg. 28, 1259\u20131265 (2022). https:\/\/doi.org\/10.1016\/j.fas.2022.05.005","journal-title":"Foot Ankle Surg."},{"key":"718_CR75","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s13246-023-01215-w","volume":"46","author":"T Kim","year":"2023","unstructured":"Kim, T., Goh, T.S., Lee, J.S., Lee, J.H., Kim, H., Jung, I.D.: Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures. Phys. Eng. Sci. Med. 46, 265\u2013277 (2023). https:\/\/doi.org\/10.1007\/s13246-023-01215-w","journal-title":"Phys. Eng. Sci. Med."},{"key":"718_CR76","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.injury.2020.09.010","volume":"52","author":"N Aghnia Farda","year":"2021","unstructured":"Aghnia Farda, N., Lai, J.-Y., Wang, J.-C., Lee, P.-Y., Liu, J.-W., Hsieh, I.-H.: Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 52, 616\u2013624 (2021). https:\/\/doi.org\/10.1016\/j.injury.2020.09.010","journal-title":"Injury"},{"key":"718_CR77","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","volume":"171","author":"YD Pranata","year":"2019","unstructured":"Pranata, Y.D., Wang, K.-C., Wang, J.-C., Idram, I., Lai, J.-Y., Liu, J.-W., Hsieh, I.-H.: Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput. Methods Programs Biomed. 171, 27\u201337 (2019). https:\/\/doi.org\/10.1016\/j.cmpb.2019.02.006","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106124","author":"J Guo","year":"2021","unstructured":"Guo, J., Mu, Y., Xue, D., Li, H., Chen, J., Yan, H., Xu, H., Wang, W.: Automatic analysis system of calcaneus radiograph: Rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation. Comput. Methods Programs Biomed. (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106124","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR79","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76866-w","author":"K Murata","year":"2020","unstructured":"Murata, K., Endo, K., Aihara, T., Suzuki, H., Sawaji, Y., Matsuoka, Y., Nishimura, H., Takamatsu, T., Konishi, T., Maekawa, A., Yamauchi, H., Kanazawa, K., Endo, H., Tsuji, H., Inoue, S., Fukushima, N., Kikuchi, H., Sato, H., Yamamoto, K.: Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci. Rep. (2020). https:\/\/doi.org\/10.1038\/s41598-020-76866-w","journal-title":"Sci. Rep."},{"key":"718_CR80","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1097\/CORR.0000000000001685","volume":"479","author":"YC Li","year":"2021","unstructured":"Li, Y.C., Chen, H.H., Horng-Shing Lu, H., Hondar Wu, H.T., Chang, M.C., Chou, P.H.: Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists? Clin. Orthop. Relat. Res. 479, 1598\u20131612 (2021). https:\/\/doi.org\/10.1097\/CORR.0000000000001685","journal-title":"Clin. Orthop. Relat. Res."},{"key":"718_CR81","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1016\/j.spinee.2021.03.006","volume":"21","author":"A Yabu","year":"2021","unstructured":"Yabu, A., Hoshino, M., Tabuchi, H., Takahashi, S., Masumoto, H., Akada, M., Morita, S., Maeno, T., Iwamae, M., Inose, H., Kato, T., Yoshii, T., Tsujio, T., Terai, H., Toyoda, H., Suzuki, A., Tamai, K., Ohyama, S., Hori, Y., Okawa, A., Nakamura, H.: Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. Spine J. 21, 1652\u20131658 (2021). https:\/\/doi.org\/10.1016\/j.spinee.2021.03.006","journal-title":"Spine J."},{"key":"718_CR82","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.3174\/ajnr.A7094","volume":"42","author":"JE Small","year":"2021","unstructured":"Small, J.E., Osler, P., Paul, A.B., Kunst, M.: CT cervical Spine fracture detection using a convolutional neural network. Am. J. Neuroradiol. 42, 1341\u20131347 (2021). https:\/\/doi.org\/10.3174\/ajnr.A7094","journal-title":"Am. J. Neuroradiol."},{"key":"718_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126946","volume":"566","author":"LW Cheng","year":"2024","unstructured":"Cheng, L.W., Chou, H.H., Cai, Y.X., Huang, K.Y., Hsieh, C.C., Chu, P.L., Cheng, I.S., Hsieh, S.Y.: Automated detection of vertebral fractures from X-ray images: a novel machine learning model and survey of the field. Neurocomputing 566, 126946 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2023.126946","journal-title":"Neurocomputing"},{"key":"718_CR84","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.OA.22.00030","author":"N Inagaki","year":"2022","unstructured":"Inagaki, N., Nakata, N., Ichimori, S., Udaka, J., Mandai, A., Saito, M.: Detection of sacral fractures on radiographs using artificial intelligence. JBJS Open Access (2022). https:\/\/doi.org\/10.2106\/JBJS.OA.22.00030","journal-title":"JBJS Open Access"},{"key":"718_CR85","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1080\/17453674.2020.1803664","volume":"91","author":"Y Yamada","year":"2020","unstructured":"Yamada, Y., Maki, S., Kishida, S., Nagai, H., Arima, J., Yamakawa, N., Iijima, Y., Shiko, Y., Kawasaki, Y., Kotani, T., Shiga, Y., Inage, K., Orita, S., Eguchi, Y., Takahashi, H., Yamashita, T., Minami, S., Ohtori, S.: Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. ACTA Orthop. 91, 699\u2013704 (2020). https:\/\/doi.org\/10.1080\/17453674.2020.1803664","journal-title":"ACTA Orthop."},{"key":"718_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e11266","volume":"8","author":"N Twinprai","year":"2022","unstructured":"Twinprai, N., Boonrod, A., Boonrod, A., Chindaprasirt, J., Sirithanaphol, W., Chindaprasirt, P., Twinprai, P.: Artificial intelligence (AI) vs. human in hip fracture detection. Heliyon 8, e11266 (2022). https:\/\/doi.org\/10.1016\/j.heliyon.2022.e11266","journal-title":"Heliyon"},{"key":"718_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109139","volume":"130","author":"G Kitamura","year":"2020","unstructured":"Kitamura, G.: Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection. Eur. J. Radiol. 130, 109139 (2020). https:\/\/doi.org\/10.1016\/j.ejrad.2020.109139","journal-title":"Eur. J. Radiol."},{"key":"718_CR88","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21311-3","author":"C-T Cheng","year":"2021","unstructured":"Cheng, C.-T., Wang, Y., Chen, H.-W., Hsiao, P.-M., Yeh, C.-N., Hsieh, C.-H., Miao, S., Xiao, J., Liao, C.-H., Lu, L.: A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat. Commun. (2021). https:\/\/doi.org\/10.1038\/s41467-021-21311-3","journal-title":"Nat. Commun."},{"key":"718_CR89","doi-asserted-by":"publisher","unstructured":"Damien, P., Nader, R.B., Yaacoub, C., Lahoud, J.-C.: Iliopectineal Line fracture detection for computer-aided acetabular fracture classification. In: 2019 Ninth Int. Conf. Image Process. Theory, Tools Appl., 2019: pp. 1\u20135. https:\/\/doi.org\/10.1109\/IPTA.2019.8936080","DOI":"10.1109\/IPTA.2019.8936080"},{"key":"718_CR90","doi-asserted-by":"publisher","DOI":"10.3390\/life13010133","author":"T Rashid","year":"2023","unstructured":"Rashid, T., Zia, M.S., Najam-ur-Rehman, T., Meraj, T., Rauf, H.T., Kadry, S.: A minority class balanced approach using the DCNN-LSTM method to detect human wrist fracture. Life-Basel (2023). https:\/\/doi.org\/10.3390\/life13010133","journal-title":"Life-Basel"},{"key":"718_CR91","doi-asserted-by":"publisher","DOI":"10.1055\/a-1577-4645","author":"F Erne","year":"2021","unstructured":"Erne, F., Dehncke, D., Herath, S., Springer, F., Pfeifer, N., Eggeling, R., K\u00fcper, M.: Correction: deep learning in the detection of rare fractures - development of a \u201cDeep Learning Convolutional Network\u201d model for detecting acetabular fractures. Z. Orthop. Unfall. (2021). https:\/\/doi.org\/10.1055\/a-1577-4645","journal-title":"Z. Orthop. Unfall."},{"key":"718_CR92","doi-asserted-by":"publisher","unstructured":"Castro-Gutierrez, E., Estacio-Cerquin, L., Gallegos-Guillen, J., Obando, J.D.: Detection of acetabulum fractures using X-ray imaging and processing methods focused on noisy images. In: 2019 Amity Int. Conf. Artif. Intell., 2019: pp. 296\u2013302. https:\/\/doi.org\/10.1109\/AICAI.2019.8701297","DOI":"10.1109\/AICAI.2019.8701297"},{"key":"718_CR93","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-021-02845-0","author":"K Oka","year":"2021","unstructured":"Oka, K., Shiode, R., Yoshii, Y., Tanaka, H., Iwahashi, T., Murase, T.: Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays. J. Orthop. Surg. Res. (2021). https:\/\/doi.org\/10.1186\/s13018-021-02845-0","journal-title":"J. Orthop. Surg. Res."},{"key":"718_CR94","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0257361","author":"JF Dipnall","year":"2021","unstructured":"Dipnall, J.F., Page, R., Du, L., Costa, M., Lyons, R.A., Cameron, P., de Steiger, R., Hau, R., Bucknill, A., Oppy, A., Edwards, E., Varma, D., Jung, M.C., Gabbe, B.J., Du, L., Lyons, R.A., Cameron, P., Steiger, R., Hau, R., Bucknill, A., Oppy, A., Edwards, E., Varma, D., Jung, M.C., Gabbe, B.J.: Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol. PLoS ONE (2021). https:\/\/doi.org\/10.1371\/journal.pone.0257361","journal-title":"PLoS ONE"},{"key":"718_CR95","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1186\/s12891-021-04260-2","volume":"22","author":"Y Sato","year":"2021","unstructured":"Sato, Y., Takegami, Y., Asamoto, T., Ono, Y., Hidetoshi, T., Goto, R., Kitamura, A., Honda, S.: Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study. BMC Musculoskelet. Disord. 22, 407 (2021). https:\/\/doi.org\/10.1186\/s12891-021-04260-2","journal-title":"BMC Musculoskelet. Disord."},{"key":"718_CR96","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106144","volume":"93","author":"A Ahmed","year":"2024","unstructured":"Ahmed, A., Imran, A.S., Manaf, A., Kastrati, Z., Daudpota, S.M.: Enhancing wrist abnormality detection with YOLO: analysis of state-of-the-art single-stage detection models. Biomed. Signal Process. Control 93, 106144 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2024.106144","journal-title":"Biomed. Signal Process. Control"},{"key":"718_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104776","volume":"137","author":"C Chen","year":"2021","unstructured":"Chen, C., Liu, B., Zhou, K., He, W., Yan, F., Wang, Z., Xiao, R.: CSR-Net: cross-scale residual network for multi-objective scaphoid fracture segmentation. Comput. Biol. Med. 137, 104776 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104776","journal-title":"Comput. Biol. Med."},{"key":"718_CR98","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/CORR.0000000000001318","volume":"478","author":"DW Langerhuizen","year":"2020","unstructured":"Langerhuizen, D.W., Bulstra, A.E., Janssen, S.J., Ring, D., Kerkhoffs, G.M., Jaarsma, R.L., Doornberg, J.N.: Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid? Clin. Orthop. Relat. Res. 478, 1 (2020). https:\/\/doi.org\/10.1097\/CORR.0000000000001318","journal-title":"Clin. Orthop. Relat. Res."},{"key":"718_CR99","doi-asserted-by":"publisher","DOI":"10.1007\/s00068-020-01468-0","author":"E Ozkaya","year":"2022","unstructured":"Ozkaya, E., Topal, F.E., Bulut, T., Gursoy, M., Ozuysal, M., Karakaya, Z.: Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur. J. Trauma Emerg. Surg. (2022). https:\/\/doi.org\/10.1007\/s00068-020-01468-0","journal-title":"Eur. J. Trauma Emerg. Surg."},{"key":"718_CR100","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1097\/RLI.0000000000000615","volume":"55","author":"JW Choi","year":"2020","unstructured":"Choi, J.W., Cho, Y.J., Lee, S., Lee, J., Lee, S., Choi, Y.H., Cheon, J.-E., Ha, J.Y.: Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Invest. Radiol. 55, 101\u2013110 (2020). https:\/\/doi.org\/10.1097\/RLI.0000000000000615","journal-title":"Invest. Radiol."},{"key":"718_CR101","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1007\/s11596-021-2501-4","volume":"41","author":"P-R Liu","year":"2021","unstructured":"Liu, P.-R., Zhang, J.-Y., Xue, M.-D., Duan, Y.-Y., Hu, J.-L., Liu, S.-X., Xie, Y., Wang, H.-L., Wang, J.-W., Huo, T.-T., Ye, Z.-W.: Artificial Intelligence to diagnose tibial plateau fractures: an intelligent assistant for orthopedic physicians. Curr. Med. Sci. 41, 1158\u20131164 (2021). https:\/\/doi.org\/10.1007\/s11596-021-2501-4","journal-title":"Curr. Med. Sci."},{"key":"718_CR102","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101937","author":"R Castro-Zunti","year":"2021","unstructured":"Castro-Zunti, R., Chae, K.J., Choi, Y., Jin, G.Y., Ko, S.: Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification. Comput. Med. Imaging Graph. (2021). https:\/\/doi.org\/10.1016\/j.compmedimag.2021.101937","journal-title":"Comput. Med. Imaging Graph."},{"key":"718_CR103","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-12453-5","author":"A Niiya","year":"2022","unstructured":"Niiya, A., Murakami, K., Kobayashi, R., Sekimoto, A., Saeki, M., Toyofuku, K., Kato, M., Shinjo, H., Ito, Y., Takei, M., Murata, C., Ohgiya, Y.: Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci. Rep. (2022). https:\/\/doi.org\/10.1038\/s41598-022-12453-5","journal-title":"Sci. Rep."},{"key":"718_CR104","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103620","volume":"75","author":"Y Gao","year":"2022","unstructured":"Gao, Y., Liu, H., Jiang, L., Yang, C., Yin, X., Coatrieux, J.-L., Chen, Y.: CCE-Net: a rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules. Biomed. Signal Process. Control 75, 103620 (2022). https:\/\/doi.org\/10.1016\/j.bspc.2022.103620","journal-title":"Biomed. Signal Process. Control"},{"key":"718_CR105","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0248809","volume":"16","author":"A Lind","year":"2021","unstructured":"Lind, A., Akbarian, E., Olsson, S., N\u00e5sell, H., Sk\u00f6ldenberg, O., Razavian, A.S., Gordon, M.: Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO\/OTA classification system. PLoS ONE 16, e0248809 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0248809","journal-title":"PLoS ONE"},{"key":"718_CR106","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1002\/jmri.28284","volume":"57","author":"TNK Hung","year":"2023","unstructured":"Hung, T.N.K., Vy, V.P.T., Tri, N.M., Hoang, L.N., Van Tuan, L., Ho, Q.T., Le, N.Q.K., Kang, J.-H.: Automatic detection of meniscus tears using backbone convolutional neural networks on knee MRI. J. Magn. Reson. Imaging 57, 740\u2013749 (2023). https:\/\/doi.org\/10.1002\/jmri.28284","journal-title":"J. Magn. Reson. Imaging"},{"key":"718_CR107","doi-asserted-by":"publisher","first-page":"104687","DOI":"10.1016\/j.bspc.2023.104687","volume":"83","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Che, Z., Li, Y., Mu, M., Gang, J., Xiao, Y., Yao, Y.: Multi-level classification of knee cartilage lesion in multimodal MRI based on deep learning. Biomed. Signal Process. Control 83, 104687 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2023.104687","journal-title":"Biomed. Signal Process. Control"},{"key":"718_CR108","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.jot.2022.05.006","volume":"34","author":"J Li","year":"2022","unstructured":"Li, J., Qian, K., Liu, J., Huang, Z., Zhang, Y., Zhao, G., Wang, H., Li, M., Liang, X., Zhou, F., Yu, X., Li, L., Wang, X., Yang, X., Jiang, Q.: Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model. J. Orthop. Transl. 34, 91\u2013101 (2022). https:\/\/doi.org\/10.1016\/j.jot.2022.05.006","journal-title":"J. Orthop. Transl."},{"key":"718_CR109","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s00256-020-03410-2","volume":"49","author":"B Fritz","year":"2020","unstructured":"Fritz, B., Marbach, G., Civardi, F., Fucentese, S.F., Pfirrmann, C.W.A.: Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol. 49, 1207\u20131217 (2020). https:\/\/doi.org\/10.1007\/s00256-020-03410-2","journal-title":"Skeletal Radiol."},{"key":"718_CR110","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.cmpb.2019.03.011","volume":"173","author":"D Hussain","year":"2019","unstructured":"Hussain, D., Han, S.-M.: Computer-aided osteoporosis detection from DXA imaging. Comput. Methods Programs Biomed. 173, 87\u2013107 (2019). https:\/\/doi.org\/10.1016\/j.cmpb.2019.03.011","journal-title":"Comput. Methods Programs Biomed."},{"key":"718_CR111","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.jhsa.2019.11.019","volume":"45","author":"N Tecle","year":"2020","unstructured":"Tecle, N., Teitel, J., Morris, M.R., Sani, N., Mitten, D., Hammert, W.C.: Convolutional neural network for second metacarpal radiographic osteoporosis screening. J. Hand Surg. Am. 45, 175\u2013181 (2020). https:\/\/doi.org\/10.1016\/j.jhsa.2019.11.019","journal-title":"J. Hand Surg. Am."},{"key":"718_CR112","doi-asserted-by":"publisher","first-page":"76649","DOI":"10.1109\/ACCESS.2021.3081915","volume":"9","author":"H El-Saadawy","year":"2021","unstructured":"El-Saadawy, H., Tantawi, M., Shedeed, H.A., Tolba, M.F.: A hybrid two-stage GNG-modified VGG method for bone X-rays classification and abnormality detection. IEEE Access 9, 76649\u201376661 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3081915","journal-title":"IEEE Access"},{"key":"718_CR113","doi-asserted-by":"publisher","DOI":"10.3390\/biology11050665","author":"G Singh","year":"2022","unstructured":"Singh, G., Anand, D., Cho, W., Joshi, G.P., Son, K.C.: Hybrid deep learning approach for automatic detection in musculoskeletal radiographs. Biology-Basel (2022). https:\/\/doi.org\/10.3390\/biology11050665","journal-title":"Biology-Basel"},{"key":"718_CR114","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1038\/s42256-019-0126-0","volume":"1","author":"M Varma","year":"2019","unstructured":"Varma, M., Lu, M., Gardner, R., Dunnmon, J., Khandwala, N., Rajpurkar, P., Long, J., Beaulieu, C., Shpanskaya, K., Fei-Fei, L., Lungren, M.P., Patel, B.N.: Automated abnormality detection in lower extremity radiographs using deep learning. Nat. Mach. Intell. 1, 578\u2013583 (2019). https:\/\/doi.org\/10.1038\/s42256-019-0126-0","journal-title":"Nat. Mach. Intell."},{"key":"718_CR115","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/reports2040026","volume":"2","author":"G Chada","year":"2019","unstructured":"Chada, G.: Machine learning models for abnormality detection in musculoskeletal radiographs. Reports 2, 26 (2019). https:\/\/doi.org\/10.3390\/reports2040026","journal-title":"Reports"},{"key":"718_CR116","doi-asserted-by":"publisher","unstructured":"Teeyapan, K.: Abnormality detection in musculoskeletal radiographs using EfficientNets, 2020 24th Int. Comput. Sci. Eng. Conf. ICSEC 2020 (2020). https:\/\/doi.org\/10.1109\/ICSEC51790.2020.9375275","DOI":"10.1109\/ICSEC51790.2020.9375275"},{"key":"718_CR117","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/ICAEE48663.2019.8975455","volume":"2019","author":"TC Mondol","year":"2019","unstructured":"Mondol, T.C., Iqbal, H., Hashem, M.M.A.: Deep CNN-based ensemble CADx model for musculoskeletal abnormality detection from radiographs, 2019 5th Int. Conf. Adv. Electr. Eng. ICAEE 2019, 392\u2013397 (2019). https:\/\/doi.org\/10.1109\/ICAEE48663.2019.8975455","journal-title":"Conf. Adv. Electr. Eng. ICAEE"},{"key":"718_CR118","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1007\/s13198-021-01580-3","volume":"13","author":"PK Mall","year":"2022","unstructured":"Mall, P.K., Singh, P.K.: BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images. Int. J. Syst. Assur. Eng. Manag. 13, 658\u2013672 (2022). https:\/\/doi.org\/10.1007\/s13198-021-01580-3","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"718_CR119","doi-asserted-by":"publisher","first-page":"9097","DOI":"10.1038\/s41598-021-88578-w","volume":"11","author":"M He","year":"2021","unstructured":"He, M., Wang, X., Zhao, Y.: A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs. Sci. Rep. 11, 9097 (2021). https:\/\/doi.org\/10.1038\/s41598-021-88578-w","journal-title":"Sci. Rep."},{"key":"718_CR120","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103402","volume":"68","author":"FR Eweje","year":"2021","unstructured":"Eweje, F.R., Bao, B., Wu, J., Dalal, D., Liao, W., He, Y., Luo, Y., Lu, S., Zhang, P., Peng, X., Sebro, R., Bai, H.X., States, L.: Deep learning for classification of bone lesions on routine MRI. EBioMedicine 68, 103402 (2021). https:\/\/doi.org\/10.1016\/j.ebiom.2021.103402","journal-title":"EBioMedicine"},{"key":"718_CR121","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2024-0153","author":"YL Khaleel","year":"2024","unstructured":"Khaleel, Y.L., Habeeb, M.A., Albahri, A.S., Al-Quraishi, T., Albahri, O.S., Alamoodi, A.H.: Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. J. Intell. Syst. (2024). https:\/\/doi.org\/10.1515\/jisys-2024-0153","journal-title":"J. Intell. Syst."},{"key":"718_CR122","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13081380","author":"AS Mohammed","year":"2023","unstructured":"Mohammed, A.S., Hasanaath, A.A., Latif, G., Bashar, A.: Knee osteoarthritis detection and severity classification using residual neural networks on preprocessed x-ray images. Diagnostics (2023). https:\/\/doi.org\/10.3390\/diagnostics13081380","journal-title":"Diagnostics"},{"key":"718_CR123","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e21703","volume":"9","author":"K Fatema","year":"2023","unstructured":"Fatema, K., Hossen, A., Azam, S., Hossain, S., Karim, A., Hasan, Z., Jonkman, M.: Heliyon development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images. Heliyon 9, e21703 (2023). https:\/\/doi.org\/10.1016\/j.heliyon.2023.e21703","journal-title":"Heliyon"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00718-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00718-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00718-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T14:12:02Z","timestamp":1734531122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00718-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":123,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["718"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00718-y","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"29 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"303"}}