{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:21:19Z","timestamp":1778221279051,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.<\/jats:p>","DOI":"10.3390\/jimaging7070105","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T04:46:57Z","timestamp":1624596417000},"page":"105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?"],"prefix":"10.3390","volume":"7","author":[{"given":"Guillaume","family":"Reichert","sequence":"first","affiliation":[{"name":"Radiology Department, Louis Mourier Hospital, Assistance Publique-H\u00f4pitaux de Paris (APHP), University of Paris, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Bellamine","sequence":"additional","affiliation":[{"name":"Radiology Department, Louis Mourier Hospital, Assistance Publique-H\u00f4pitaux de Paris (APHP), University of Paris, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthieu","family":"Fontaine","sequence":"additional","affiliation":[{"name":"Radiology Department, Louis Mourier Hospital, Assistance Publique-H\u00f4pitaux de Paris (APHP), University of Paris, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beatrice","family":"Naipeanu","sequence":"additional","affiliation":[{"name":"Radiology Department, Louis Mourier Hospital, Assistance Publique-H\u00f4pitaux de Paris (APHP), University of Paris, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrien","family":"Altar","sequence":"additional","affiliation":[{"name":"Emergency Department, Louis Mourier Hospital, AP-HP, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elodie","family":"Mejean","sequence":"additional","affiliation":[{"name":"Emergency Department, Foch Hospital, 92150 Suresnes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Javaud","sequence":"additional","affiliation":[{"name":"Emergency Department, Louis Mourier Hospital, AP-HP, 92700 Colombes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathalie","family":"Siauve","sequence":"additional","affiliation":[{"name":"Radiology Department, Louis Mourier Hospital, Assistance Publique-H\u00f4pitaux de Paris (APHP), University of Paris, 92700 Colombes, France"},{"name":"INSERM, U970, Paris Cardiovascular Research Center\u2014PARCC, 75015 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moonen, P.J., Mercelina, L., Boer, W., and Fret, T. (2017). Diagnostic error in the Emergency Department: Follow up of patients with minor trauma in the outpatient clinic. Scand. J. Traum. Resusc. Emerg. Med., 25.","DOI":"10.1186\/s13049-017-0361-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1080\/02841850600806340","article-title":"Systematic analysis of missed extremity fractures in emergency radiology","volume":"47","author":"Wei","year":"2006","journal-title":"Acta Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_5","first-page":"1","article-title":"Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images","volume":"11","author":"Oh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Al-Amidie, M., Al-Asadi, A., Humaidi, A.J., Al-Shamma, O., Fadhel, M.A., Zhang, J., Santamar\u00eda, J., and Duan, Y. (2021). Novel transfer learning approach for medical imaging with limited labeled data. Cancers, 13.","DOI":"10.3390\/cancers13071590"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s41568-020-00327-9","article-title":"Designing deep learning studies in cancer diagnostics","volume":"21","author":"Kleppe","year":"2021","journal-title":"Nat. Rev. Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/17453674.2019.1711323","article-title":"Deep learning in fracture detection: A narrative review","volume":"91","author":"Kalmet","year":"2020","journal-title":"Acta Orthop."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1080\/17453674.2017.1387732","article-title":"Will intelligent machine learning revolutionize orthopedic imaging?","volume":"88","author":"Berg","year":"2017","journal-title":"Acta Orthop."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kandel, I., Castelli, M., and Popovi\u010d, A. (2020). Musculoskeletal Images Classification for Detection of Fractures Using Transfer Learning. J. Imaging, 6.","DOI":"10.3390\/jimaging6110127"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e216096","DOI":"10.1001\/jamanetworkopen.2021.6096","article-title":"Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs","volume":"4","author":"Yoon","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","article-title":"Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms\u2014Are they on par with humans for diagnosing fractures?","volume":"88","author":"Olczak","year":"2017","journal-title":"Acta Orthop."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA."},{"key":"ref_16","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","article-title":"Automated detection and classification of the proximal humerus fracture by using deep learning algorithm","volume":"89","author":"Chung","year":"2018","journal-title":"Acta Orthop."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: Transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1080\/17453674.2019.1600125","article-title":"Artificial intelligence detection of distal radius fractures: A comparison between the convolutional neural network and professional assessments","volume":"90","author":"Gan","year":"2019","journal-title":"Acta Orthop."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109139","DOI":"10.1016\/j.ejrad.2020.109139","article-title":"Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection","volume":"130","author":"Kitamura","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","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., and Lu, L. (2021). A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat. Commun., 12.","DOI":"10.1038\/s41467-021-21311-3"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-00352-w","article-title":"Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs","volume":"3","author":"Jones","year":"2020","journal-title":"Npj Digit. Med."},{"key":"ref_23","unstructured":"Buhrmester, V., M\u00fcnch, D., and Arens, M. (2019). Analysis of explainers of black box deep neural networks for computer vision: A survey. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tobler, P., Cyriac, J., Kovacs, B.K., Hofmann, V., Sexauer, R., Paciolla, F., Stieltjes, B., Amsler, F., and Hirschmann, A. (2021). AI-based detection and classification of distal radius fractures using low-effort data labeling: Evaluation of applicability and effect of training set size. Eur. Radiol.","DOI":"10.1007\/s00330-021-07811-2"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Raisuddin, A.M., Vaattovaara, E., Nevalainen, M., Nikki, M., J\u00e4rvenp\u00e4\u00e4, E., Makkonen, K., Pinola, P., Palsio, T., Niemensivu, A., and Tervonen, O. (2021). Critical evaluation of deep neural networks for wrist fracture detection. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-85570-2"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/7\/105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:46Z","timestamp":1760163826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/7\/105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":25,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["jimaging7070105"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7070105","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}