An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound.
Green classification of cystocele on dynamic transperineal ultrasound (TPUS) remains operator-dependent because it requires manual frame selection and landmark-based assessment of the Valsalva maneuver. We developed a workflow-oriented AI-assisted clinical decision support system for automated ureth...
| Publicado en: | Journal of Medical Systems Vol. 50; no. 1; pp. 1 - 16 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
9/1/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196666784&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196666784 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 9/1/2026 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196666784 196666784 196666784 10.1007/s10916-026-02453-7 196666784 ppf: 1 ppct: 15 formats: tig: atl: An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound. aug: au: Zhu, Hongjie Geng, Xin Zhou, Huayuan Guo, Wei Dai, Yin Zhang, Hao Dong, Meng Li, Hong Wang, Xinlu affil: https://ror.org/03awzbc87 College of Medicine and Biological Information Engineering, Northeastern University, 110167, Shenyang, China sug: subj: Artificial Intelligence Decision Support Systems, Clinical Cystocele Classification Pelvic Floor Muscles Ultrasonography Image Processing, Computer Assisted Human Retrospective Design Prediction Models Descriptive Statistics Data Analysis Software Confidence Intervals Wilcoxon Signed Rank Test Kruskal-Wallis Test Fisher's Exact Test ab: Green classification of cystocele on dynamic transperineal ultrasound (TPUS) remains operator-dependent because it requires manual frame selection and landmark-based assessment of the Valsalva maneuver. We developed a workflow-oriented AI-assisted clinical decision support system for automated urethrovesical junction localization and dynamic Green classification and prospectively evaluated its standalone and reader-support performance. This diagnostic accuracy and reader study included 881 patients from a tertiary referral hospital, comprising a retrospective development cohort (n = 688) and an independent prospective test cohort (n = 193). A nested subset of 67 prospective patients was used for a reader study involving two junior and two intermediate radiologists under unaided and AI-assisted conditions. In the complete prospective test cohort, Green-AttGRU achieved a macro-averaged AUC of 0.939 (95% CI, 0.897–0.971) and an overall accuracy of 0.902 (95% CI, 0.860–0.943). In the reader study, overall accuracy increased from 0.761 to 0.821 without AI to 0.851–0.881 with AI, while macro-F1 increased from 0.660 to 0.777 to 0.820–0.860. Overall inter-reader agreement increased from a Fleiss' κ of 0.453 to 0.786, and pooled median interpretation time decreased from 26.7 s to 9.9 s. These findings support the preliminary feasibility of the system as a workflow-oriented decision-support tool for dynamic TPUS interpretation. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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