{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:31:34Z","timestamp":1775766694170,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"2021 National Orthopedics and Sports Rehabilitation Clinical Medical Research Center Innovation Fund Project","award":["2021-NCRC-CXJJ-PY-20"],"award-info":[{"award-number":["2021-NCRC-CXJJ-PY-20"]}]},{"name":"2021 National Orthopedics and Sports Rehabilitation Clinical Medical Research Center Innovation Fund Project","award":["2021-NCRC-CXJJ-ZH-11"],"award-info":[{"award-number":["2021-NCRC-CXJJ-ZH-11"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>To develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Including 313 patients aged 16 \u2013 65\u00a0years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-01091-6","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T05:01:45Z","timestamp":1694408505000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules"],"prefix":"10.1186","volume":"23","author":[{"given":"Chen","family":"Liang","sequence":"first","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Minglei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yingkai","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Ren","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiangning","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jinping","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Songcen","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"issue":"3","key":"1091_CR1","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/s00167-005-0679-9","volume":"14","author":"VB Duthon","year":"2006","unstructured":"Duthon VB, Barea C, Abrassart S, Fasel JH, Fritschy D, M\u00e9n\u00e9trey J. Anatomy of the anterior cruciate ligament. Knee Surg Sports Traumatol Arthrosc. 2006;14(3):204\u201313. https:\/\/doi.org\/10.1007\/s00167-005-0679-9. (Epub 2005 Oct 19 PMID: 16235056).","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"1091_CR2","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-319-22345-2_10","volume":"881","author":"A Negahi Shirazi","year":"2015","unstructured":"Negahi Shirazi A, Chrzanowski W, Khademhosseini A, Dehghani F. Anterior cruciate ligament: structure, injuries and regenerative treatments. Adv Exp Med Biol. 2015;881:161\u201386. https:\/\/doi.org\/10.1007\/978-3-319-22345-2_10. (PMID: 26545750).","journal-title":"Adv Exp Med Biol"},{"issue":"1","key":"1091_CR3","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1177\/03635465980260012201","volume":"26","author":"BR Bach Jr","year":"1998","unstructured":"Bach BR Jr, Levy ME, Bojchuk J, Tradonsky S, Bush-Joseph CA, Khan NH. Single-incision endoscopic anterior cruciate ligament reconstruction using patellar tendon autograft. Minimum two-year follow-up evaluation. Am J Sports Med. 1998;26(1):30\u201340. https:\/\/doi.org\/10.1177\/03635465980260012201. (PMID: 9474398).","journal-title":"Am J Sports Med."},{"issue":"24","key":"1091_CR4","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1056\/NEJMcp1805931","volume":"380","author":"V Musahl","year":"2019","unstructured":"Musahl V, Karlsson J. Anterior cruciate ligament tear. N Engl J Med. 2019;380(24):2341\u20138. https:\/\/doi.org\/10.1056\/NEJMcp1805931. (PMID: 31189037).","journal-title":"N Engl J Med"},{"issue":"1","key":"1091_CR5","doi-asserted-by":"publisher","first-page":"7583","DOI":"10.1038\/s41598-017-08133-4","volume":"7","author":"K Li","year":"2017","unstructured":"Li K, Du J, Huang LX, Ni L, Liu T, Yang HL. The diagnostic accuracy of magnetic resonance imaging for anterior cruciate ligament injury in comparison to arthroscopy: a meta-analysis. Sci Rep. 2017;7(1):7583. https:\/\/doi.org\/10.1038\/s41598-017-08133-4. (PMID:28790406;PMCID:PMC5548790).","journal-title":"Sci Rep"},{"issue":"2","key":"1091_CR6","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574\u201382. https:\/\/doi.org\/10.1148\/radiol.2017162326. (Epub 2017 Apr 24 PMID: 28436741).","journal-title":"Radiology"},{"issue":"5","key":"1091_CR7","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep Convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285\u201398. https:\/\/doi.org\/10.1109\/TMI.2016.2528162. (Epub 2016 Feb 11. PMID: 26886976; PMCID: PMC4890616).","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"1091_CR8","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1002\/mp.13361","volume":"46","author":"M Byra","year":"2019","unstructured":"Byra M, Galperin M, Ojeda-Fournier H, Olson L, O\u2019Boyle M, Comstock C, Andre M. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys. 2019;46(2):746\u201355. https:\/\/doi.org\/10.1002\/mp.13361. (Epub 2019 Jan 16. PMID: 30589947; PMCID: PMC8544811).","journal-title":"Med Phys"},{"issue":"3","key":"1091_CR9","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1121\/10.0001924","volume":"148","author":"J Shao","year":"2020","unstructured":"Shao J, Zheng J, Zhang B. Deep convolutional neural networks for thyroid tumor grading using ultrasound b-mode images. J Acoust Soc Am. 2020;148(3):1529. https:\/\/doi.org\/10.1121\/10.0001924. (PMID: 33003892).","journal-title":"J Acoust Soc Am"},{"issue":"6","key":"1091_CR10","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.3390\/s20061753","volume":"20","author":"H El-Khatib","year":"2020","unstructured":"El-Khatib H, Popescu D, Ichim L. Deep learning-based methods for automatic diagnosis of skin lesions. Sensors (Basel). 2020;20(6):1753. https:\/\/doi.org\/10.3390\/s20061753. (PMID:32245258;PMCID:PMC7147720).","journal-title":"Sensors (Basel)"},{"issue":"12","key":"1091_CR11","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1016\/j.diii.2020.04.011","volume":"101","author":"P Blanc-Durand","year":"2020","unstructured":"Blanc-Durand P, Schiratti JB, Schutte K, Jehanno P, Herent P, Pigneur F, Lucidarme O, Benaceur Y, Sadate A, Luciani A, Ernst O, Rouchaud A, Creze M, Dallongeville A, Banaste N, Cadi M, Bousaid I, Lassau N, Jegou S. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagn Interv Imaging. 2020;101(12):789\u201394. https:\/\/doi.org\/10.1016\/j.diii.2020.04.011. (Epub 2020 May 22 PMID: 32451309).","journal-title":"Diagn Interv Imaging"},{"issue":"4","key":"1091_CR12","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, Giret Y, Bou Antoun M, Morillot C, Chassin X, Cotten A, Zerbib J, Fournier L. Artificial intelligence to diagnose meniscus tears on MRI. Diagn Interv Imaging. 2019;100(4):243\u20139. https:\/\/doi.org\/10.1016\/j.diii.2019.02.007. (Epub 2019 Mar 28 PMID: 30928472).","journal-title":"Diagn Interv Imaging"},{"key":"1091_CR13","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6:60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0.","journal-title":"J Big Data"},{"key":"1091_CR14","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume":"2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition,\" 2016. IEEE Conference Computer Vision Pattern Recog (CVPR). 2016;2016:770\u20138. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","journal-title":"IEEE Conference Computer Vision Pattern Recog (CVPR)"},{"issue":"3","key":"1091_CR15","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TII.2020.2993842","volume":"17","author":"X Li","year":"2021","unstructured":"Li X, Jiang Y, Li M, Yin S. Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Industr Inf. 2021;17(3):1958\u201367. https:\/\/doi.org\/10.1109\/TII.2020.2993842.","journal-title":"IEEE Trans Industr Inf"},{"key":"1091_CR16","doi-asserted-by":"publisher","unstructured":"Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo and Q. Hu, \"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,\" 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 11531\u201311539 https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1091_CR17","doi-asserted-by":"publisher","unstructured":"J. Hu, L. Shen and G. Sun, \"Squeeze-and-Excitation Networks,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 7132\u20137141 https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2022","key":"1091_CR18","doi-asserted-by":"publisher","first-page":"8390997","DOI":"10.1155\/2022\/8390997","volume":"14","author":"Z Li","year":"2022","unstructured":"Li Z, Wang H, Han Q, Liu J, Hou M, Chen G, Tian Y, Weng T. Convolutional neural network with multiscale fusion and attention mechanism for skin diseases assisted diagnosis. Comput Intell Neurosci. 2022;14(2022):8390997. https:\/\/doi.org\/10.1155\/2022\/8390997. (PMID:35747726;PMCID:PMC9213118).","journal-title":"Comput Intell Neurosci"},{"issue":"12","key":"1091_CR19","doi-asserted-by":"publisher","first-page":"e0261285","DOI":"10.1371\/journal.pone.0261285","volume":"16","author":"L Xu","year":"2021","unstructured":"Xu L, Wang L, Cheng S, Li Y. MHANet: a hybrid attention mechanism for retinal diseases classification. PLoS One. 2021;16(12):e0261285. https:\/\/doi.org\/10.1371\/journal.pone.0261285. (PMID: 34914763; PMCID: PMC8675717).","journal-title":"PLoS One"},{"issue":"1","key":"1091_CR20","doi-asserted-by":"publisher","first-page":"15103","DOI":"10.1038\/s41598-022-18879-1","volume":"12","author":"P Zhou","year":"2022","unstructured":"Zhou P, Cao Y, Li M, Ma Y, Chen C, Gan X, Wu J, Lv X, Chen C. HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism. Sci Rep. 2022;12(1):15103. https:\/\/doi.org\/10.1038\/s41598-022-18879-1. (PMID:36068309;PMCID:PMC9448811).","journal-title":"Sci Rep"},{"issue":"12","key":"1091_CR21","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.3390\/cancers11121901","volume":"11","author":"H Yao","year":"2019","unstructured":"Yao H, Zhang X, Zhou X, Liu S. Parallel structure deep neural network using cnn and rnn with an attention mechanism for breast cancer histology image classification. Cancers (Basel). 2019;11(12):1901. https:\/\/doi.org\/10.3390\/cancers11121901. (PMID:31795390;PMCID:PMC6966545).","journal-title":"Cancers (Basel)"},{"key":"1091_CR22","doi-asserted-by":"publisher","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 6000\u20136010. https:\/\/doi.org\/10.48550\/arXiv.1706.03762.","DOI":"10.48550\/arXiv.1706.03762"},{"key":"1091_CR23","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.cmpb.2016.12.006","volume":"140","author":"I \u0160tajduhar","year":"2017","unstructured":"\u0160tajduhar I, Mamula M, Mileti\u0107 D, \u00dcnal G. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed. 2017;140:151\u201364. https:\/\/doi.org\/10.1016\/j.cmpb.2016.12.006. (Epub 2016 Dec 15 PMID: 28254071).","journal-title":"Comput Methods Programs Biomed"},{"issue":"11","key":"1091_CR24","doi-asserted-by":"publisher","first-page":"e1002699","DOI":"10.1371\/journal.pmed.1002699","volume":"15","author":"N Bien","year":"2018","unstructured":"Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, Bereket M, Patel BN, Yeom KW, Shpanskaya K, Halabi S, Zucker E, Fanton G, Amanatullah DF, Beaulieu CF, Riley GM, Stewart RJ, Blankenberg FG, Larson DB, Jones RH, Langlotz CP, Ng AY, Lungren MP. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med. 2018;15(11):e1002699. https:\/\/doi.org\/10.1371\/journal.pmed.1002699. (PMID: 30481176; PMCID: PMC6258509).","journal-title":"PLoS Med."},{"key":"1091_CR25","doi-asserted-by":"publisher","unstructured":"I. Irmakci, S. M. Anwar, D. A. Torigian and U. Bagci, \"Deep Learning for Musculoskeletal Image Analysis,\" 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 1481\u20131485, https:\/\/doi.org\/10.1109\/IEEECONF44664.2019.9048671.","DOI":"10.1109\/IEEECONF44664.2019.9048671"},{"key":"1091_CR26","unstructured":"Tsai C-H, Kiryati N, Konen E, Eshed I, Mayer A. Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet). In: Tal A, Ismail Ben A, Marleen de B, Maxime D, Herve L, Christopher P, editors. Proceedings of the Third Conference on Medical Imaging with Deep Learning; Proceedings of Machine Learning Research: PMLR; 2020. p. 784--94"},{"issue":"1","key":"1091_CR27","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/diagnostics11010105","volume":"11","author":"MJ Awan","year":"2021","unstructured":"Awan MJ, Rahim MSM, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH. Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach. Diagnostics (Basel). 2021;11(1):105. https:\/\/doi.org\/10.3390\/diagnostics11010105. (PMID:33440798;PMCID:PMC7826961).","journal-title":"Diagnostics (Basel)"},{"issue":"9","key":"1091_CR28","doi-asserted-by":"publisher","first-page":"232596712110275","DOI":"10.1177\/23259671211027543","volume":"9","author":"I Tamimi","year":"2021","unstructured":"Tamimi I, Ballesteros J, Lara AP, Tat J, Alaqueel M, Schupbach J, Marwan Y, Urdiales C, Gomez-de-Gabriel JM, Burman M, Martineau PA. A prediction model for primary anterior cruciate ligament injury using artificial intelligence. Orthop J Sports Med. 2021;9(9):23259671211027544. https:\/\/doi.org\/10.1177\/23259671211027543. (PMID:34568504;PMCID:PMC8461131).","journal-title":"Orthop J Sports Med"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01091-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-01091-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01091-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T16:14:20Z","timestamp":1700410460000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-01091-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1091"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-01091-6","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,11]]},"assertion":[{"value":"22 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. The study was approved by the Ethics Committee of the 2nd Affiliated Hospital of Harbin Medical University in China (KY2022-233).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"120"}}