Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.

Saved in:
Bibliographic Details
Title: Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.
Authors: Kim SM; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea., Shin HK; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea., Bae SE; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea., Kim JM; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea., Lee YH; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea., Chong GO; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea., Lee J; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea., Park JC; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea., Lee HJ; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea., Park SY; Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea., Lee J; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea. gyjhlee@knu.ac.kr., Nam WJ; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea. nwj0612@knu.ac.kr.
Source: Scientific reports [Sci Rep] 2026 May 07. Date of Electronic Publication: 2026 May 07.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 42098354
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Kim+SM%22">Kim SM</searchLink>; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Shin+HK%22">Shin HK</searchLink>; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Bae+SE%22">Bae SE</searchLink>; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+JM%22">Kim JM</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+YH%22">Lee YH</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Chong+GO%22">Chong GO</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+J%22">Lee J</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+JC%22">Park JC</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+HJ%22">Lee HJ</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+SY%22">Park SY</searchLink>; Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, 41404, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+J%22">Lee J</searchLink>; Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, 41944, Republic of Korea. gyjhlee@knu.ac.kr.<br /><searchLink fieldCode="AU" term="%22Nam+WJ%22">Nam WJ</searchLink>; School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea. nwj0612@knu.ac.kr.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2026 May 07. <i>Date of Electronic Publication: </i>2026 May 07.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=42098354
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1038/s41598-026-50736-3
    Languages:
      – Code: eng
        Text: English
    Titles:
      – TitleFull: Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kim SM
      – PersonEntity:
          Name:
            NameFull: Shin HK
      – PersonEntity:
          Name:
            NameFull: Bae SE
      – PersonEntity:
          Name:
            NameFull: Kim JM
      – PersonEntity:
          Name:
            NameFull: Lee YH
      – PersonEntity:
          Name:
            NameFull: Chong GO
      – PersonEntity:
          Name:
            NameFull: Lee J
      – PersonEntity:
          Name:
            NameFull: Park JC
      – PersonEntity:
          Name:
            NameFull: Lee HJ
      – PersonEntity:
          Name:
            NameFull: Park SY
      – PersonEntity:
          Name:
            NameFull: Lee J
      – PersonEntity:
          Name:
            NameFull: Nam WJ
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 07
              M: 05
              Text: 2026 May 07
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 2045-2322
          Titles:
            – TitleFull: Scientific reports
              Type: main
ResultId 1