Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists.
To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology...
| Published in: | Journal of Digital Imaging Vol. 37; no. 2; pp. 444 - 455 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177625996&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177625996 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177625996 177625996 177625996 10.1007/s10278-023-00946-2 177625996 ppf: 444 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. aug: au: Xing, Zhaoyu Zhu, Zuhui Jiang, Zhenxing Zhao, Jingshi Chen, Qin Xing, Wei Pan, Liang Zeng, Yan Liu, Aie Ding, Jiule affil: https://ror.org/051jg5p78 Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China sug: subj: Automation Urinary Calculi Radiography Tomography, X-Ray Computed Methods Radiography, Abdominal Radiographic Image Interpretation, Computer-Assisted Radiologists Stress, Occupational Prevention and Control Human Retrospective Design Pelvis Radiography False Positive Results ROC Curve Precision Descriptive Statistics Sensitivity and Specificity Artificial Intelligence ab: To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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