{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:37:56Z","timestamp":1791909476752,"version":"4.3.4"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T00:00:00Z","timestamp":1541635200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001782","name":"University of Melbourne","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001782","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013741","name":"Royal Australian and New Zealand College of Radiologists","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013741","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Med Imag Rad Onc"],"published-print":{"date-parts":[[2019,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      To evaluate the accuracy of deep convolutional neural networks (\n                      <jats:styled-content style=\"fixed-case\">DCNN<\/jats:styled-content>\n                      s) for detecting neck of femur (NoF) fractures on radiographs, in comparison with perceptual training in medically\u2010na\u00efve individuals.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study extends a previous study that conducted perceptual training in medically\u2010na\u00efve individuals for the detection of NoF fractures on a variety of dataset sizes. The same anteroposterior hip radiograph dataset was used to train two\n                      <jats:styled-content style=\"fixed-case\">DCNN<\/jats:styled-content>\n                      s (AlexNet and GoogLeNet) to detect NoF fractures. For direct comparison with perceptual training results, deep learning was completed across a variety of dataset sizes (200, 320 and 640 images) with images split into training (80%) and validation (20%). An additional 160 images were used as the final test set. Multiple pre\u2010processing and augmentation techniques were utilised.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      AlexNet and GoogLeNet\n                      <jats:styled-content style=\"fixed-case\">DCNN<\/jats:styled-content>\n                      s NoF fracture detection accuracy increased with larger training dataset sizes and mildly with augmentation. Accuracy increased from 81.9% and 88.1% to 89.4% and 94.4% for AlexNet and GoogLeNet respectively. Similarly, the test accuracy for the perceptual training in top\u2010performing medically\u2010na\u00efve individuals increased from 87.6% to 90.5% when trained on 640 images compared with 200 images.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Single detection tasks in radiology are commonly used in\n                      <jats:styled-content style=\"fixed-case\">DCNN<\/jats:styled-content>\n                      research with their results often used to make broader claims about machine learning being able to perform as well as subspecialty radiologists. This study suggests that as impressive as recognising fractures is for a\n                      <jats:styled-content style=\"fixed-case\">DCNN<\/jats:styled-content>\n                      , similar learning can be achieved by top\u2010performing medically\u2010na\u00efve humans with less than 1 hour of perceptual training.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1754-9485.12828","type":"journal-article","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T10:09:01Z","timestamp":1541671741000},"page":"27-32","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2323-3504","authenticated-orcid":false,"given":"Matthew","family":"Adams","sequence":"first","affiliation":[{"name":"Radiology Department Royal Melbourne Hospital Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-2484","authenticated-orcid":false,"given":"Weijia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Psychological Sciences University of Melbourne Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Holcdorf","sequence":"additional","affiliation":[{"name":"Radiology Department Royal Melbourne Hospital Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark W","family":"McCusker","sequence":"additional","affiliation":[{"name":"Radiology Department Royal Melbourne Hospital 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