{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:54:11Z","timestamp":1775537651888,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the performance of a CNN\u2010based (convolutional neural networks\u2010based) AI software for automatic recognition and measurement of minimal levator hiatus in transperineal ultrasound volumes from postpartum women, and to assess its agreement with manual measurements in different functional states.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective analysis of 100 transperineal ultrasound volumes measured independently by two sonographers (one junior, one senior) using a SonoScape S60 ultrasound system. Manual measurements included anteroposterior diameter (AP), left\u2010to\u2010right diameter (LR), levator hiatus area (HA), which were assessed at rest, during maximum pelvic floor contraction maneuver, and during maximum Valsalva maneuver. The levator\u2010urethra gap (LUG) was additionally measured during contraction. The same volumes were identified and measured using automated CNN\u2010based software (Auto PF software, SonoScape). When automatic identification failed, manual slice adjustment was permitted before reattempting automated measurement. The automatic recognition rate was recorded in different functional states. Inter\u2010rater reliability between manual and automated measurements was evaluated using intraclass correlation coefficient (ICC) and Bland\u2013Altman analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The overall automatic recognition rate was 86.69%, varying by functional state: 94% at rest, 86.17% during contraction, 79.80% during Valsalva. Automated measurements showed good agreement with manual measurements for HA, AP and LR (ICC\u2009&gt;\u20090.75), excellent agreement with the senior sonographer's measurement for HA, AP (ICC\u2009&gt;\u20090.90). The senior sonographer's measurements demonstrated higher concordance with automated results. Furthermore, the agreement was poorest for LUG measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automated software is feasible for the recognition and measurement of LH in postpartum women, which can reduce the inter\u2010observer variability, standardize pelvic floor assessment and improve workflow efficiency. Our study supports the clinical utility of CNN\u2010based AI ultrasound software, but optimization is needed for LUG measurements to enhance clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.70139","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T07:32:56Z","timestamp":1764142376000},"page":"1001-1010","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Recognition and Measurement for Levator Hiatus in\n                    <scp>3D<\/scp>\n                    Ultrasound"],"prefix":"10.1002","volume":"45","author":[{"given":"Fanhua","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Ultrasound Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00192-020-04639-0"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2022.04.046"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ogc.2015.10.008"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.5355"},{"key":"e_1_2_10_6_1","first-page":"1093","article-title":"Detection of subpubic tumor causing bladder outlet obstruction by 3D\u2009perineal 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study on the practicality of the semi\u2010automatic measurement for the urogenital hiatus [abstract]","volume":"52","author":"Wang H","year":"2018","journal-title":"Ultrasound Obstet Gynecol"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20181"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2022.100412"},{"key":"e_1_2_10_20_1","article-title":"Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self\u2010normalizing neural network","volume":"5","author":"Bonmati E","year":"2018","journal-title":"J Med Imaging 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