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However, detection of small stress lesions is complicated by the low quality of SPECT, leading to significant interreader variability. The study objectives were to develop an approach based on a deep convolutional neural network (CNN) for detecting lumbar lesions in <jats:sup>99m<\/jats:sup>Tc\u2010MDP scans and to compare its performance to that of physicians in a localization receiver operating characteristic (LROC) study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Sixty\u2010five lesion\u2010absent (LA) <jats:sup>99m<\/jats:sup>Tc\u2010MDP studies performed in pediatric patients for evaluating LBP were retrospectively identified. Projections for an artificial focal lesion were acquired separately by imaging a <jats:sup>99m<\/jats:sup>Tc capillary tube at multiple distances from the collimator. An approach was developed to automatically insert lesions into LA scans to obtain realistic lesion\u2010present (LP) <jats:sup>99m<\/jats:sup>Tc\u2010MDP images while ensuring knowledge of the ground truth. A deep CNN was trained using 2.5D views extracted in LP and LA <jats:sup>99m<\/jats:sup>Tc\u2010MDP image sets. During testing, the CNN was applied in a sliding\u2010window fashion to compute a 3D \u201cheatmap\u201d reporting the probability of a lesion being present at each lumbar location. The algorithm was evaluated using cross\u2010validation on a <jats:sup>99m<\/jats:sup>Tc\u2010MDP test dataset which was also studied by five physicians in a LROC study. LP images in the test set were obtained by incorporating lesions at sites selected by a physician based on clinical likelihood of injury in this population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The deep learning (DL) system slightly outperformed human observers, achieving an area under the LROC curve (AUC<jats:sub>LROC<\/jats:sub>) of 0.830 (95% confidence interval [CI]: [0.758, 0.924]) compared with 0.785 (95% CI: [0.738, 0.830]) for physicians. The AUC<jats:sub>LROC<\/jats:sub> for the DL system was higher than that of two readers (difference in AUC<jats:sub>LROC<\/jats:sub> [\u0394AUC<jats:sub>LROC<\/jats:sub>]\u00a0=\u00a00.049 and 0.053) who participated to the study and slightly lower than that of two other readers (\u0394AUC<jats:sub>LROC<\/jats:sub>\u00a0=\u00a0\u22120.006 and \u22120.012). Another reader outperformed DL by a more substantial margin (\u0394AUC<jats:sub>LROC<\/jats:sub>\u00a0=\u00a0\u22120.053).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The DL system provides comparable or superior performance than physicians in localizing small <jats:sup>99m<\/jats:sup>Tc\u2010MDP positive lumbar lesions.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15033","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T01:35:00Z","timestamp":1623202500000},"page":"4249-4261","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Detecting lumbar lesions in <sup>99m<\/sup>Tc\u2010MDP SPECT by deep learning: Comparison with physicians"],"prefix":"10.1002","volume":"48","author":[{"given":"Yoann","family":"Petibon","sequence":"first","affiliation":[{"name":"Gordon Center of Medical Imaging Massachusetts General Hospital Boston Massachusetts USA"},{"name":"Department of Radiology Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederic","family":"Fahey","sequence":"additional","affiliation":[{"name":"Department of Radiology Harvard Medical School Boston Massachusetts USA"},{"name":"Division of Nuclear Medicine and Molecular Imaging Boston Children's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Cao","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging Boston Children's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zakhar","family":"Levin","sequence":"additional","affiliation":[{"name":"Gordon Center of Medical Imaging Massachusetts General Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Briana","family":"Sexton\u2010Stallone","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging Boston Children's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Falone","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging Boston Children's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Zukotynski","sequence":"additional","affiliation":[{"name":"Departments of Medicine and Radiology McMaster University Hamilton Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha","family":"Kwatra","sequence":"additional","affiliation":[{"name":"Department of Radiology Harvard Medical School Boston Massachusetts USA"},{"name":"Division of Nuclear Medicine and Molecular Imaging Boston Children's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Lim","sequence":"additional","affiliation":[{"name":"Gordon Center of Medical Imaging Massachusetts General Hospital Boston Massachusetts USA"},{"name":"Department of Radiology Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zvi","family":"Bar\u2010Sever","sequence":"additional","affiliation":[{"name":"Institute of Nuclear Medicine Schneider Children's Medical Center of Israel Petah Tikva Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanis","family":"Chemli","sequence":"additional","affiliation":[{"name":"Gordon Center of Medical Imaging Massachusetts General Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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