{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T04:31:35Z","timestamp":1747801895630,"version":"3.41.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01306-4","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T19:01:54Z","timestamp":1729623714000},"page":"1563-1580","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy"],"prefix":"10.1007","volume":"38","author":[{"given":"Sriramulu","family":"Devarapaga","sequence":"first","affiliation":[]},{"given":"Rajesh","family":"Thumma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"issue":"8","key":"1306_CR1","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.1109\/TBME.2008.921171","volume":"55","author":"BJ Fregly","year":"2008","unstructured":"B. J. Fregly, \u201cComputational Assessment of Combinations of Gait Modifications for Knee Osteoarthritis Rehabilitation,\u201d IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 2104-2106, Aug. 2008.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"6","key":"1306_CR2","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TNSRE.2019.2915812","volume":"27","author":"SH Kang","year":"2019","unstructured":"S. H. Kang, S. J. Lee, J. M. Press, and L. -Q. Zhang, \u201cReal-Time Three-Dimensional Knee Moment Estimation in Knee Osteoarthritis: Toward Biodynamic Knee Osteoarthritis Evaluation and Training,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1263-1272, June 2019.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"3","key":"1306_CR3","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1109\/TBME.2007.905388","volume":"55","author":"N Mezghani","year":"2008","unstructured":"N. Mezghani et al., \u201cAutomatic Classification of Asymptomatic and Osteoarthritis Knee Gait Patterns Using Kinematic Data Features and the Nearest Neighbor Classifier,\u201d IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1230-1232, March 2008.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"1306_CR4","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1109\/TBME.2007.912428","volume":"55","author":"K Turcot","year":"2008","unstructured":"K. Turcot, R. Aissaoui, K. Boivin, M. Pelletier, N. Hagemeister and J. A. de Guise, \u201cNew Accelerometric Method to Discriminate Between Asymptomatic Subjects and Patients With Medial Knee Osteoarthritis During 3-D Gait,\u201d IEEE Transactions on Biomedical Engineering, vol. 55, no. 4, pp. 1415-1422, April 2008.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"1306_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2021.3137628","volume":"10","author":"A Sorriento","year":"2022","unstructured":"A. Sorriento et al., \u201cDesign, Development, and Validation of a Knee Brace to Standardize the US Imaging Evaluation of Knee Osteoarthritis,\u201d IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8, 2022.","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"issue":"9","key":"1306_CR6","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1109\/TBME.2007.891934","volume":"54","author":"BJ Fregly","year":"2007","unstructured":"B. J. Fregly, J. A. Reinbolt, K. L. Rooney, K. H. Mitchell, and T. L. Chmielewski, \u201cDesign of patient-specific gait modifications for knee osteoarthritis rehabilitation,\u201d IEEE Transactions on Biomedical Engineering, vol. 54, no. 9, pp. 1687-1695, Sept. 2007.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"9","key":"1306_CR7","doi-asserted-by":"publisher","first-page":"2976","DOI":"10.1109\/TMI.2020.2985861","volume":"39","author":"Y Nasser","year":"2020","unstructured":"Y. Nasser, R. Jennane, A. Chetouani, E. Lespessailles, and M. E. Hassouni, \u201cDiscriminative Regularized Auto-Encoder for Early Detection of Knee Osteo Arthritis: Data from the Osteoarthritis Initiative,\u201d IEEE Transactions on Medical Imaging, vol. 39, no. 9, pp. 2976-2984, Sept. 2020.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"8","key":"1306_CR8","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1109\/10.855942","volume":"47","author":"Ju-Hong Lee","year":"2000","unstructured":"Ju-Hong Lee, Ching-Chuan Jiang, and Tung-Tai Yuan, \u201cVibration arthrometry in patients with knee joint disorders,\u201d IEEE Transactions on Biomedical Engineering, vol. 47, no. 8, pp. 1131-1133, Aug. 2000.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"1306_CR9","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/TNSRE.2013.2291203","volume":"22","author":"SH Kang","year":"2014","unstructured":"S. H. Kang, S. J. Lee, Y. Ren and L. -Q. Zhang, \u201cReal-Time Knee Adduction Moment Feedback Training Using an Elliptical Trainer,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 2, pp. 334-343, March 2014.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"11","key":"1306_CR10","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1109\/TBME.2010.2058112","volume":"57","author":"P Dodin","year":"2010","unstructured":"P. Dodin, J. -P. Pelletier, J. Martel-Pelletier and F. Abram, \u201cAutomatic Human Knee Cartilage Segmentation From 3-D Magnetic Resonance Images,\u201d IEEE Transactions on Biomedical Engineering, vol. 57, no. 11, pp. 2699-2711, Nov. 2010.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"3","key":"1306_CR11","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/JBHI.2021.3102090","volume":"26","author":"Y Wang","year":"2022","unstructured":"Y. Wang et al., \u201cLearning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data from the Osteoarthritis Initiative,\u201d IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 1239-1250, March 2022.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"11","key":"1306_CR12","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1109\/TMI.2022.3181060","volume":"41","author":"K Hu","year":"2022","unstructured":"K. Hu, W. Wu, W. Li, M. Simic, A. Zomaya and Z. Wang, \u201cAdversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction,\u201d IEEE Transactions on Medical Imaging, vol. 41, no. 11, pp. 3207-3217, Nov. 2022.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1306_CR13","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/TNSRE.2020.3043831","volume":"29","author":"K Kubota","year":"2021","unstructured":"K. Kubota et al., \u201cUsefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 239-248, 2021.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"8","key":"1306_CR14","doi-asserted-by":"publisher","first-page":"2412","DOI":"10.1109\/TBME.2020.3041512","volume":"68","author":"M Eugster","year":"2021","unstructured":"M. Eugster et al., \u201cQuantitative Evaluation of the Thickness of the Available Manipulation Volume Inside the Knee Joint Capsule for Minimally Invasive Robotic Unicondylar Knee Arthroplasty,\u201d IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2412-2422, Aug. 2021.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"1306_CR15","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1109\/TBME.2018.2837620","volume":"66","author":"RA Bloomfield","year":"2019","unstructured":"R. A. Bloomfield, M. C. Fennema, K. A. McIsaac, and M. G. Teeter, \u201cProposal and Validation of a Knee Measurement System for Patients With Osteoarthritis,\u201d IEEE Transactions on Biomedical Engineering, vol. 66, no. 2, pp. 319-326, Feb. 2019.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"1306_CR16","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1109\/TBME.2008.2006025","volume":"56","author":"L Shamir","year":"2009","unstructured":"L. Shamir et al., \u201cKnee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis,\u201d IEEE Transactions on Biomedical Engineering, vol. 56, no. 2, pp. 407-415, Feb. 2009.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"1306_CR17","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/TBME.2020.3024285","volume":"68","author":"C Yiallourides","year":"2021","unstructured":"C. Yiallourides and P. A. Naylor, \u201cTime-Frequency Analysis and Parameterisation of Knee Sounds for Non-Invasive Detection of Osteoarthritis,\u201d IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, pp. 1250-1261, April 2021.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"12","key":"1306_CR18","doi-asserted-by":"publisher","first-page":"4346","DOI":"10.1109\/TMI.2020.3017007","volume":"39","author":"HH Nguyen","year":"2020","unstructured":"H. H. Nguyen, S. Saarakkala, M. B. Blaschko, and A. Tiulpin, \u201cSemixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs,\u201d IEEE Transactions on Medical Imaging, vol. 39, no. 12, pp. 4346-4356, Dec. 2020.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1306_CR19","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1186\/s12891-021-04722-7","volume":"22","author":"Simon Olsson","year":"2021","unstructured":"Simon Olsson, Ehsan Akbarian, Anna Lind, Ali Sharif Razavian& Max Gordon, \u201cAutomating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population\u201d, BMC Musculoskeletal Disorders, vol. 22, pp. 844 2021.","journal-title":"BMC Musculoskeletal Disorders"},{"key":"1306_CR20","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/s13075-021-02634-4","volume":"23","author":"Jean-Baptiste Schiratti","year":"2021","unstructured":"Jean-Baptiste Schiratti, R\u00e9my Dubois, Paul Herent, David Cahan\u00e9, \u201cA deep learning method for predicting knee osteoarthritis radiographic progression from MRI\u201d, Arthritis Research & Therapy, vol. 23, pp. 262, 2021.","journal-title":"Arthritis Research & Therapy"},{"key":"1306_CR21","doi-asserted-by":"publisher","first-page":"104334","DOI":"10.1016\/j.compbiomed.2021.104334","volume":"133","author":"Albert Swiecicki","year":"2021","unstructured":"Albert Swiecicki, Nianyi Li, Jonathan O'Donnell, Nicholas Said, Jichen Yang, \u201cDeep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists\u201d, Computers in Biology and Medicine, vol. 133, pp. 104334, June 2021.","journal-title":"Computers in Biology and Medicine"},{"key":"1306_CR22","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.joca.2020.05.002","volume":"28","author":"SJO Rytky","year":"2020","unstructured":"S.J.O. Rytky, A. Tiulpin, T. Frondelius, M.A.J. Finnila, \u201cAutomating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography\u201d, Osteoarthritis and Cartilage, vol. 28, pp. 1133-1144, August 2020.","journal-title":"Osteoarthritis and Cartilage"},{"key":"1306_CR23","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1186\/s13018-022-03429-2","volume":"17","author":"Jianfeng Yang","year":"2022","unstructured":"Jianfeng Yang, Quanbo Ji, Ming Ni, Guoqiang Zhang & Yan Wang, \u201cAutomatic assessment of knee osteoarthritis severity in portable devices based on deep learning\u201d, Journal of Orthopaedic Surgery and Research, vol. 17, pp.540, 2022.","journal-title":"Journal of Orthopaedic Surgery and Research"},{"key":"1306_CR24","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1038\/s41598-018-20132-7","volume":"8","author":"J\u00e9r\u00f4meThevenot AlekseiTiulpin","year":"2018","unstructured":"AlekseiTiulpin, J\u00e9r\u00f4meThevenot, EsaRahtu, Petri Lehenkari& Simo Saarakkala, \u201cAutomatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach\u201d, Scientific Reports, vol. 8, pp. 1727, 2018.","journal-title":"Scientific Reports"},{"key":"1306_CR25","doi-asserted-by":"crossref","unstructured":"Osman Altay, Elif Varol Altay, \u201cInvestigation of Slime Mould Algorithm and Hybrid Slime Mould Algorithms'Performance in Global Optimization Problems\u201d, DUJE (Dicle University Journal of Engineering), vol. 13:4, pp. 661\u2013671, 2022.","DOI":"10.24012\/dumf.1177288"},{"key":"1306_CR26","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1007\/s10462-019-09732-5","volume":"53","author":"HA Alsattar","year":"2020","unstructured":"H. A. Alsattar, A. A. Zaidan& B. B. Zaidan, \u201cNovel meta-heuristic bald eagle search optimisation algorithm\u201d, Artificial Intelligence Review, vol. 53, pp.2237\u20132264, 2020.","journal-title":"Artificial Intelligence Review"},{"key":"1306_CR27","unstructured":"Muhammad Kashif Yaqoob, Syed Farooq Ali, Muhammad Bilal, Muhammad Shehzad Hanif, and Ubaid M,\u201dResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection,\u201d sensors, 2021."},{"key":"1306_CR28","doi-asserted-by":"crossref","unstructured":"Shagun Sharma, Kalpna Guleria, Sunita Tiwari, Sushil Kumar,\u201dA deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans,\u201d Sensors, vol.24, 2022.","DOI":"10.1016\/j.measen.2022.100506"},{"key":"1306_CR29","doi-asserted-by":"crossref","unstructured":"Shervan Fekri-Ershad,Mustafa Jawad Al-Imari, Mohammed Hayder Hamad, Marwa Fadhil Alsafar, Fuad Ghazi Hassan, Mazin Eidan Hadi, and Karrar Salih Mahd,\u201dCell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet,\u201d Research Article, 2022.","DOI":"10.1155\/2022\/6895833"},{"key":"1306_CR30","doi-asserted-by":"crossref","unstructured":"Emad Haq Qazi, Abdulrazaq Almorjan and Tanveer Zia,\u201dA One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection,\u201d Applied Sciences, vol.12, 2022.","DOI":"10.3390\/app12167986"},{"key":"1306_CR31","doi-asserted-by":"crossref","unstructured":"S. Adnan, M. R. Islam, M. Shafiullah, S. Hoque and M. S. Azam, \u201cBald Eagle Search Optimization Algorithm For Economic Dispatch Problem With Renewable Energy Integration,\u201d International Scientific Technical Conference Alternating Current Electric Drives (ACED), pp. 1\u20136, 2023.","DOI":"10.1109\/ACED57798.2023.10143440"},{"key":"1306_CR32","doi-asserted-by":"crossref","unstructured":"Pavel Trojovsky and Mohammad Dehghani,\u201d Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications,\u201d Sensors, vol. 22, issue.3, 2022.","DOI":"10.3390\/s22030855"},{"key":"1306_CR33","doi-asserted-by":"crossref","unstructured":"Mosa, Diana T, Amena Mahmoud, John Zaki, Shaymaa E Sorour, Shaker El-Sappagh, and Tamer Abuhmed, \u201cHenry gas solubility optimization double machine learning classifier for neurosurgical patients,\u201d Plos one, vol.18, no. 5, 2023.","DOI":"10.1371\/journal.pone.0285455"},{"key":"1306_CR34","doi-asserted-by":"crossref","unstructured":"Yuanfei Wei, Zalinda Othman, Kauthar Mohd Daud, Shihong Yin, Qifang Luo and Yongquan Zhou,\u201dEquilibrium Optimizer and Slime Mould Algorithm with Variable Neighborhood Search for Job Shop Scheduling Problem,\u201d Mathematics, vol.10, issue 21, 2022.","DOI":"10.3390\/math10214063"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01306-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01306-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01306-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T17:26:38Z","timestamp":1747761998000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01306-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["1306"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01306-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"29 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}