{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:03:01Z","timestamp":1791504181333,"version":"4.3.4"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning\u2010based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low\u2010dose CT or tested on a large\u2010scale external\u00a0dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient\u2010level labels were used as an external validation set. A 3D U\u2010Net model was employed to segment the kidneys, followed by gradient\u2010based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive\u00a0regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low\u2010dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (). For patient\u2010level classification, the system achieved an area under the receiver\u2010operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney\u00a0stones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our deep\u2010learning\u2010based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation\u00a0set.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15518","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:08:58Z","timestamp":1644797338000},"page":"2545-2554","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans"],"prefix":"10.1002","volume":"49","author":[{"given":"Daniel C.","family":"Elton","sequence":"first","affiliation":[{"name":"Radiology and Imaging Sciences National Institutes of Health Clinical Center  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evrim B.","family":"Turkbey","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences National Institutes of Health Clinical Center  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perry J.","family":"Pickhardt","sequence":"additional","affiliation":[{"name":"School of Medicine and Public Health University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences National Institutes of Health Clinical Center  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2012.03.052"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.172.6.10350277"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12632"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/ki.2012.419"},{"key":"e_1_2_9_6_1","unstructured":"AdairIILB.Kidney Stone Detection on CT.2020.https:\/\/www.acrdsi.org\/DSI\u2010Services\/Define\u2010AI\/Use\u2010Cases\/Kidney\u2010Stone\u2010Detection\u2010on\u2010CT."},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2009.11.047"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2014.05.089"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-018-1719-5"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2021.05.004"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1523-1755.2005.00599.x"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrurol.2011.9"},{"key":"e_1_2_9_13_1","first-page":"278","article-title":"Economic impact of urinary stones","volume":"3","author":"Hyams ES","year":"2014","journal-title":"Transl Androl Urol"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.178.1.1780101"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-5242-9"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2014.08.031"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2020.07.025"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109267"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1089\/end.2005.19.856"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2012.03.009"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2011.07.091"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1590\/s1677-5538.ibju.2018.0819"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-020-02865-0"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-009-9181-0"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4903056"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.04.021"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2019180066"},{"key":"e_1_2_9_28_1","first-page":"436","article-title":"Automatic detection and scoring of kidney stones on noncontrast CT images using S.T.O.N.E. 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