{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:40:59Z","timestamp":1784295659494,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Key Research and Development Program of Shaanxi Province,China","award":["2021SF-092"],"award-info":[{"award-number":["2021SF-092"]}]},{"name":"Innovation Team Project of Natural Science Fund of Shaanxi Province, China","award":["2019TD-018"],"award-info":[{"award-number":["2019TD-018"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model\u2019s performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists\u2019 readings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists\u2019 workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-023-00975-x","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T05:04:04Z","timestamp":1675055044000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study"],"prefix":"10.1186","volume":"23","author":[{"given":"Jiangfen","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nijun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianrui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanwang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaoling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayao","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinli","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"975_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/cc13873","volume":"18","author":"C Battle","year":"2014","unstructured":"Battle C, Lovett S, Hutchings H, Evans PA. Predicting outcomes after blunt chest wall trauma: development and external validation of a new prognostic model. Crit Care. 2014;18:1\u2013182.","journal-title":"Crit Care"},{"key":"975_CR2","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.cjtee.2020.04.003","volume":"23","author":"BN Dogrul","year":"2020","unstructured":"Dogrul BN, Kiliccalan I, Asci ES, Peker SC. Blunt trauma related chest wall and pulmonary injuries: an overview. Chin J Traumatol. 2020;23:125\u201338.","journal-title":"Chin J Traumatol"},{"key":"975_CR3","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/s1010-7940(02)00813-8","volume":"23","author":"ST Liman","year":"2003","unstructured":"Liman ST, Kuzucu A, Tastepe AI, Ulasan GN, Topcu S. Chest injury due to blunt trauma. Eur J Cardiothorac Surg. 2003;23:374\u20138.","journal-title":"Eur J Cardiothorac Surg"},{"key":"975_CR4","doi-asserted-by":"publisher","first-page":"e000441","DOI":"10.1136\/tsaco-2020-000441","volume":"5","author":"J Peek","year":"2020","unstructured":"Peek J, Ochen Y, Saillant N, Groenwold RHH, Leenen LPH, Uribe-Leitz T, et al. Traumatic rib fractures: a marker of severe injury. A nationwide study using the National Trauma Data Bank. Trauma Surg Acute Care Open. 2020;5:e000441.","journal-title":"Trauma Surg Acute Care Open"},{"key":"975_CR5","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1097\/00005373-199412000-00018","volume":"37","author":"DW Ziegler","year":"1994","unstructured":"Ziegler DW, Agarwal NN. The morbidity and mortality of rib fractures. J Trauma. 1994;37:975\u20139.","journal-title":"J Trauma"},{"key":"975_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13049-017-0368-y","volume":"25","author":"CY Chien","year":"2017","unstructured":"Chien CY, Chen YH, Han ST, Blaney GN, Huang TS, Chen KF. The number of displaced rib fractures is more predictive for complications in chest trauma patients. Scand J Trauma Resusc Emerg Med. 2017;25:1\u201310.","journal-title":"Scand J Trauma Resusc Emerg Med"},{"key":"975_CR7","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1148\/radiol.14140583","volume":"275","author":"HB Harvey","year":"2015","unstructured":"Harvey HB, Gilman MD, Wu CC, Cushing MS, Halpern EF, Zhao J, et al. Diagnostic yield of recommendations for chest CT examination prompted by outpatient chest radiographic findings. Radiology. 2015;275:262.","journal-title":"Radiology"},{"key":"975_CR8","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1097\/RTI.0000000000000113","volume":"29","author":"TS Henry","year":"2014","unstructured":"Henry TS, Kirsch J, Kanne JP, Chung JH, Donnelly EF, Ginsburg ME, et al. ACR Appropriateness Criteria\u00ae rib fractures. J Thorac Imaging. 2014;29:364\u20136.","journal-title":"J Thorac Imaging"},{"key":"975_CR9","first-page":"444","volume":"19","author":"D Siela","year":"2008","unstructured":"Siela D. Chest radiograph evaluation and interpretation. AACN Adv Crit Care. 2008;19:444\u201373.","journal-title":"AACN Adv Crit Care"},{"key":"975_CR10","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.jacr.2013.12.019","volume":"11","author":"JH Chung","year":"2014","unstructured":"Chung JH, Cox CW, Mohammed T-LH, Kirsch J, Brown K, Dyer DS, et al. ACR appropriateness criteria blunt chest trauma. J Am Coll Radiol. 2014;11:345\u201351.","journal-title":"J Am Coll Radiol"},{"key":"975_CR11","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.ajem.2006.03.022","volume":"24","author":"S Davis","year":"2006","unstructured":"Davis S, Affatato A. Blunt chest trauma: utility of radiological evaluation and effect on treatment patterns. Am J Emerg Med. 2006;24:482\u20136.","journal-title":"Am J Emerg Med"},{"key":"975_CR12","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/S0735-6757(97)90004-8","volume":"15","author":"I Dubinsky","year":"1997","unstructured":"Dubinsky I, Low A. Non-life-threatening blunt chest trauma: appropriate investigation and treatment. Am J Emerg Med. 1997;15:240\u20133.","journal-title":"Am J Emerg Med"},{"key":"975_CR13","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.2017171734","volume":"285","author":"CE Kahn Jr","year":"2017","unstructured":"Kahn CE Jr. From images to actions: opportunities for artificial intelligence in radiology. Radiology. 2017;285:719\u201320.","journal-title":"Radiology"},{"key":"975_CR14","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122\u201331.","journal-title":"Cell"},{"key":"975_CR15","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","volume":"29","author":"HA Haenssle","year":"2018","unstructured":"Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29:1836\u201342.","journal-title":"Ann Oncol"},{"key":"975_CR16","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9:611\u201329.","journal-title":"Insights Imaging"},{"key":"975_CR17","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/MSP.2010.936730","volume":"27","author":"MN Wernick","year":"2010","unstructured":"Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC. Machine learning in medical imaging. IEEE Signal Process Mag. 2010;27:25\u201338.","journal-title":"IEEE Signal Process Mag"},{"key":"975_CR18","doi-asserted-by":"publisher","first-page":"754","DOI":"10.2214\/AJR.16.17224","volume":"208","author":"M Kohli","year":"2017","unstructured":"Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. Am J Roentgenol. 2017;208:754\u201360.","journal-title":"Am J Roentgenol"},{"key":"975_CR19","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1148\/radiol.2016150063","volume":"281","author":"M Liang","year":"2016","unstructured":"Liang M, Tang W, Xu DM, Jirapatnakul AC, Reeves AP, Henschke CI, et al. Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers. Radiology. 2016;281:279\u201388.","journal-title":"Radiology"},{"key":"975_CR20","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"Lu F, Wu F, Hu P, Peng Z, Kong D. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg. 2017;12:171\u201382.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"975_CR21","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T, Litjens G, van Ginneken B, Gubern-M\u00e9rida A, S\u00e1nchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303\u201312.","journal-title":"Med Image Anal"},{"key":"975_CR22","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","volume":"73","author":"DH Kim","year":"2018","unstructured":"Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73:439\u201345.","journal-title":"Clin Radiol"},{"key":"975_CR23","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","volume":"89","author":"SW Chung","year":"2018","unstructured":"Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Yoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89:468\u201373.","journal-title":"Acta Orthop"},{"key":"975_CR24","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","volume":"88","author":"J Olczak","year":"2017","unstructured":"Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms\u2014Are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88:581\u20136.","journal-title":"Acta Orthop"},{"key":"975_CR25","first-page":"66","volume":"216","author":"A Bg","year":"2022","unstructured":"Bg A, Jy B, Sw A, Gz A, Yz A, Xw A, Mw A. Automatic detection and localization of thighbone fractures in X-ray based on improved deep learning method. Comput Vis Image Underst. 2022;216:66.","journal-title":"Comput Vis Image Underst"},{"key":"975_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.103106","volume":"62","author":"L Jin","year":"2020","unstructured":"Jin L, Yang J, Kuang K, Ni B, Gao Y, Sun Y, Gao P, Ma W, Tan M, Kang H. Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. EBioMedicine. 2020;62: 103106.","journal-title":"EBioMedicine"},{"key":"975_CR27","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3348\/kjr.2019.0653","volume":"21","author":"T Weikert","year":"2020","unstructured":"Weikert T, Noordtzij LA, Bremerich J, Stieltjes B, Parmar V, Cyriac J, Sommer G, Sauter AW. Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography. Korean J Radiol. 2020;21:891.","journal-title":"Korean J Radiol"},{"key":"975_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110434","volume":"154","author":"C Yang","year":"2022","unstructured":"Yang C, Wang J, Xu J, Huang C, Liu F, Sun W, Hong R, Zhang L, Ma D, Li Z. Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography. Eur J Radiol. 2022;154: 110434.","journal-title":"Eur J Radiol"},{"key":"975_CR29","first-page":"28","volume":"66","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J. Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 2015;66:28.","journal-title":"Adv Neural Inf Process Syst"},{"key":"975_CR30","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D. Libra r-cnn: towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2019; p. 821\u201330.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"975_CR31","doi-asserted-by":"crossref","unstructured":"Zhang H, Chang H, Ma B, Wang N, Chen X. Dynamic R-CNN: towards high quality object detection via dynamic training. In: Computer vision-ECCV; 2020. p. 12360.","DOI":"10.1007\/978-3-030-58555-6_16"},{"key":"975_CR32","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection. In: IEEE\/CVF conference on computer vision and pattern recognition; 2018. p. 6154\u201362.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"975_CR33","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. 2022;75: 103620.","journal-title":"Biomed Signal Process Control"},{"key":"975_CR34","unstructured":"Redmon J, Farhadi A. Yolov3: an incremental improvement. 2018. arXiv preprint arXiv:1804.02767."},{"key":"975_CR35","first-page":"66","volume":"6","author":"MS Staege","year":"2016","unstructured":"Staege MS. Gene expression music algorithm-based characterization of the Ewing sarcoma stem cell signature. Stem Cells Int. 2016;6:66.","journal-title":"Stem Cells Int"},{"key":"975_CR36","doi-asserted-by":"publisher","first-page":"2868","DOI":"10.3390\/s17122868","volume":"17","author":"M Sun","year":"2017","unstructured":"Sun M, Wang Y, le Bastard C, Pan J, Ding Y. Signal subspace smoothing technique for time delay estimation using MUSIC algorithm. Sensors. 2017;17:2868.","journal-title":"Sensors"},{"key":"975_CR37","doi-asserted-by":"crossref","unstructured":"Kim K-J, Kim P-K, Chung Y-S, Choi D-H. Performance enhancement of yolov3 by adding prediction layers with spatial pyramid pooling for vehicle detection. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS); 2018. p. 1\u20136.","DOI":"10.1109\/AVSS.2018.8639438"},{"key":"975_CR38","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neuroimage.2017.08.030","volume":"162","author":"C Liao","year":"2017","unstructured":"Liao C, Bilgic B, Manhard MK, Zhao B, Cao X, Zhong J, et al. 3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction. Neuroimage. 2017;162:13\u201322.","journal-title":"Neuroimage"},{"key":"975_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep41004","volume":"7","author":"P-H Tsui","year":"2017","unstructured":"Tsui P-H, Chen CK, Kuo WH, Chang KJ, Fang J, Ma HY, Chou D. Small-window parametric imaging based on information entropy for ultrasound tissue characterization. Sci Rep. 2017;7:1\u201317.","journal-title":"Sci Rep"},{"key":"975_CR40","doi-asserted-by":"publisher","first-page":"S514","DOI":"10.1097\/TA.0b013e3182754654","volume":"73","author":"KM Ivey","year":"2012","unstructured":"Ivey KM, White CE, Wallum TE, Aden JK, Cannon JW, Chung KK. Thoracic injuries in US combat casualties: a 10-year review of Operation Enduring Freedom and Iraqi Freedom. J Trauma Acute Care Surg. 2012;73:S514\u20139.","journal-title":"J Trauma Acute Care Surg"},{"key":"975_CR41","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1148\/rg.2017160100","volume":"37","author":"BS Talbot","year":"2017","unstructured":"Talbot BS, Gange CP Jr, Chaturvedi A, Klionsky N, Hobbs SK, Chaturvedi A. Traumatic rib injury: patterns, imaging pitfalls, complications, and treatment. Radiographics. 2017;37:628\u201351.","journal-title":"Radiographics"},{"key":"975_CR42","unstructured":"Crandall J, Kent R, Patrie J, Fertile J, Martin P. Rib fracture patterns and radiologic detection\u2013a restraint-based comparison. In: Annual proceedings\/association for the advancement of automotive medicine. Association for the Advancement of Automotive Medicine; 2000. p. 235."},{"key":"975_CR43","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.2337\/dc18-0147","volume":"41","author":"Z Li","year":"2018","unstructured":"Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabet Care. 2018;41:2509\u201316.","journal-title":"Diabet Care"},{"key":"975_CR44","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402\u201310.","journal-title":"JAMA"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00975-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-00975-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00975-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T05:10:28Z","timestamp":1675055428000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-00975-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["975"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-00975-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1995864\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]},"assertion":[{"value":"25 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 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":"This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the institutional review board of The First Affiliated Hospital of Xi\u2019an Jiaotong University (Xi\u2019an, China). Informed consent was obtained from all participants and\/or their legal guardians.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18"}}