{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:55:16Z","timestamp":1781762116102,"version":"3.54.5"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pelvic floor dysfunction (PFD) is a common functional disorder following total hysterectomy. Currently, the assessment mainly relies on manual measurements of transperineal ultrasound (TPUS) images, which suffers from low efficiency, subjectivity, and limited reproducibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To address these limitations, this work proposes an automated transperineal ultrasound analysis using deep learning for pelvic floor dysfunction assessment after total hysterectomy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A labeled dataset including key anatomical landmarks such as the symphysis pubis, bladder neck, urethra, and puborectalis muscle was established, and a multi-scale shifted window Transformer was developed to achieve automatic segmentation and key point detection. Additionally, a geometric reasoning module was further designed to compute seven clinically relevant functional parameters, including bladder neck\u2013symphysis distance, posterior urethrovesical angle, and urethral rotation angle.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results demonstrated that the model achieved an average Dice coefficient of 88.67 \u00b1 1.96% in segmentation, with key point localization errors controlled within 2\u00a0mm. The automatic measurement results are highly consistent with manual annotations, with Pearson correlation coefficients up to 0.92, and effectively distinguished functional differences among patients undergoing different surgical approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed method enables structured, automated, and objective TPUS image analysis, significantly reducing manual intervention. Although validated in patients with benign diseases, the approach is directly transferable to gynecologic oncology patients, who are at even higher risk of PFD due to more extensive surgery and adjuvant therapies. It provides a reliable tool for postoperative functional monitoring and therapeutic evaluation after total hysterectomy, and holds great potential for functional imaging-based rehabilitation assessment in post-hysterectomy patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1730276","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:36:32Z","timestamp":1781760992000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated transperineal ultrasound analysis using deep learning for pelvic floor dysfunction assessment after total hysterectomy"],"prefix":"10.3389","volume":"16","author":[{"given":"Yanqing","family":"Xu","sequence":"first","affiliation":[{"name":"Ultrasonography Department, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People\u2019s Hospital","place":["Nantong, 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