{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:39:26Z","timestamp":1789472366923,"version":"build-2803163510"},"reference-count":20,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2007,10,18]],"date-time":"2007-10-18T00:00:00Z","timestamp":1192665600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Adequately defining the anatomical and functional defects in women with stress urinary incontinence prior to surgical repair is essential. Differences in dynamic changes of the bladder neck on perineal ultrasound have been shown to be effective in differentiating between women with and those without urodynamic stress incontinence (USI). This study evaluated a system to diagnose USI with computer\u2010aided vector\u2010based perineal ultrasound.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We evaluated 48 women with lower urinary tract symptoms, of whom 36 were found to have USI, by urodynamic study and computer\u2010aided vector\u2010based perineal ultrasound. The function and morphology of the lower urinary tract were used as features in a multilayer perception (MLP) neural network classifier to distinguish between women with and those without USI. The <jats:italic>k<\/jats:italic>\u2010fold cross\u2010validation method was used to estimate the performance of the proposed computer\u2010aided diagnosis (CAD) system.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The accuracy of the proposed CAD system for classifying USI was 91.7% (44\/48), the sensitivity was 94.4% (34\/36), the specificity was 83.3% (10\/12), the positive predictive value was 94.4% (34\/36) and the negative predictive value was 83.3% (10\/12). The receiver\u2013operating characteristics area index for the proposed CAD system was 0.941 \u00b1 0.034. The proposed CAD system differentiated USI effectively with perineal ultrasound.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Computer\u2010aided vector\u2010based perineal ultrasound is valuable in assessing anatomical changes of the bladder neck. Women with and those without USI can be distinguished using perineal sonographic analysis with the proposed CAD system. Because the MLP model is trainable, it could be optimized if a larger set of perineal ultrasound images and other useful features were supplied. Copyright \u00a9 2007 ISUOG. Published by John Wiley &amp; Sons, Ltd.<\/jats:p><\/jats:sec>","DOI":"10.1002\/uog.4102","type":"journal-article","created":{"date-parts":[[2007,10,18]],"date-time":"2007-10-18T15:46:54Z","timestamp":1192722414000},"page":"1002-1006","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Computer\u2010aided diagnosis of urodynamic stress incontinence with vector\u2010based perineal ultrasound using neural networks"],"prefix":"10.1002","volume":"30","author":[{"given":"Y.\u2010L.","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.\u2010Y.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2007,10,18]]},"reference":[{"key":"e_1_2_5_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01902383"},{"key":"e_1_2_5_3_2","first-page":"269","article-title":"Ultrasound cystourethrography by perineal scanning for the assessment of female stress urinary incontinence","volume":"68","author":"Kohorn EI","year":"1986","journal-title":"Obstet Gynecol"},{"key":"e_1_2_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(89)90115-4"},{"key":"e_1_2_5_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1471-0528.1992.tb13729.x"},{"key":"e_1_2_5_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/0029-7844(94)00369-O"},{"key":"e_1_2_5_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0096(199705)25:4<189::AID-JCU6>3.0.CO;2-A"},{"key":"e_1_2_5_8_2","doi-asserted-by":"publisher","DOI":"10.1067\/mob.2001.116373"},{"key":"e_1_2_5_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/00016340600880951"},{"key":"e_1_2_5_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3835(94)90089-2"},{"key":"e_1_2_5_11_2","first-page":"156","volume-title":"Neural Networks: A Comprehensive Foundation","author":"Haykin S","year":"1999"},{"key":"e_1_2_5_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90032-Z"},{"key":"e_1_2_5_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_5_14_2","unstructured":"WeissSM KapouleasI.An empirical comparison of pattern recognition neural nets and machine learning classification methods.Proc11thInt Joint Conf Artificial Intelligence1989;234\u2013237."},{"key":"e_1_2_5_15_2","unstructured":"MetzCE. 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