{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T23:20:35Z","timestamp":1789514435499,"version":"build-2803163510"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,1,7]],"date-time":"2024-01-07T00:00:00Z","timestamp":1704585600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study was to evaluate the application of automatic measurement based on convolutional neural network (CNN) technology in intracavitary ultrasound cine of anterior pelvic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 500 patients who underwent pelvic floor ultrasound examination at Peking University Shenzhen Hospital from July 2021 to February 2022 were retrospectively retrieved by the picture archiving and communication system (PACS) system, and 300 cases were used as a training set. The training set was labeled by three experienced ultrasound physicians to train CNN models and develop an automatic measurement software. The remaining 200 cases were used as a test set. Automatic measurement software identified relevant anatomical structures frame by frame and determined the two frames with the greatest difference, calculated the bladder neck descent (BND), urethral rotation angle (URA), and retrovesical angle (RA). Meanwhile, two experienced ultrasound physicians evaluated the resting frame and the maximum Valsalva frame on the cines by manual visual evaluation, labeled the anatomical structures in the corresponding frame, such as the inferoposterior margin of pubic symphysis, the mid\u2010axis of pubic symphysis, bladder contour, and urethra in the front, and calculated BND, URA, and RA. Considering that the residual urine volume (RUV) in the bladder may affect the results, enrolled patients were grouped according to the RUV (10\u201350\u2009mL, 50\u2013100\u2009mL, and &gt;100\u2009mL). The consistency of the results by automatic measurement and manual visual evaluation was evaluated using the intraclass correlation coefficient (ICC) and the Bland\u2013Altman graph.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 200 cases in the test set, 120 cases were successfully identified by the CNN automatic software with a 60% recognition rate. In the case of successful identification, the ICC of manual visual evaluation measurement and automatic measurement was 0.936 (BND), 0.911 (URA), 0.756 (RA in rest), and 0.877 (RA at maximum Valsalva), respectively. In addition, the RUV had a negligible effect on the consistency. The Bland\u2013Altman plot shows the proportion of samples outside the limit was below 5%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CNN\u2010based automatic measurement software exhibited high reliability in anterior pelvic measurement, which results in a significantly enhanced measurement efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.16392","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T01:48:41Z","timestamp":1704678521000},"page":"671-681","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2575-8678","authenticated-orcid":false,"given":"Ni\u2010ya","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Ultrasound Peking University Shenzhen Hospital  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao\u2010kun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Peking University Shenzhen Hospital  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi\u2010duo","family":"Lu","sequence":"additional","affiliation":[{"name":"National\u2010Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University  Shenzhen China"},{"name":"Shenzhen RayShape Medical Technology Co., Ltd  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin\u2010ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Peking University Shenzhen Hospital  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui\u2010jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Peking University Shenzhen Hospital  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9772-8185","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Peking University Shenzhen Hospital  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,7]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00192-019-03954-5"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0034-1383044"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2019.12.006"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14377"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpobgyn.2018.06.006"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.17390"},{"key":"e_1_2_9_8_1","first-page":"895","article-title":"A feasibility study of the automatic cystocele severity grading software for quantutative evaluation of proplapse of bladder posterior wall by transperineal ultrasound","volume":"27","author":"Wang H","year":"2018","journal-title":"Chin J Ultrason"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.09.033"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15006"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TUFFC.2022.3162800"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101951"},{"key":"e_1_2_9_13_1","first-page":"1009","article-title":"Progress of artificial intelligence in pelvic floor ultrasound","volume":"29","author":"Wu S","year":"2020","journal-title":"Chin J Ultrason"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Chen LC Zhu Y Papandreou G Schroff F Adam H.Encoder decoder with atrous separable convolution for semantic image segmentation. 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