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Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models

29. juni 2021 opdateret af: Peking University Third Hospital
The purpose of this study is to study the injury of the anterior talofibular ligament by deep learning method and compare a variety of different deep learning models to establish a deep learning method that can accurately identify and grade the injury of anterior talofibular ligament, and obtain a model with better recognition and grading effect.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

  1. Recognition and segmentation of anterior talofibular ligament based on DenseNet. Densenet was used to recognize the axial T2-fs image, and the image level was the most typical one. The labelimg program based on Python was used to locate the coordinates of the anterior talofibular ligament and then imported into Python for learning. All the data were divided into a training set (70%, and then 30% of the training set was selected as the verification set). The remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the anterior talofibular ligament, the local clipping and amplification are carried out to remove the redundant information. Finally, input the result to the next step.
  2. Establishment and comparison of various deep learning models: four deep learning models were established and compared in this study, namely VGG19, AlexNet, CapsNet, and GoogleNet. The models using image fitting alone and those combining with clinical physical examination data were compared for each deep learning model. The diagnostic efficiency between models was expressed by the ROC curve, including AUC, F1 score, etc. the ROC curve was further analyzed by t-test, Delong test, and other statistical methods. In this study, the data were divided into a training set (70%, 30% in the training set as the validation set), and the remaining 30% as the test set to evaluate the classification accuracy.

Undersøgelsestype

Observationel

Tilmelding (Forventet)

1000

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Undersøgelse Kontakt Backup

Studiesteder

  • Kina
    • Beijing
      • Beijing, Beijing, Kina, 010
        • Rekruttering
        • Peking University Third Hospital
        • Kontakt:

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Barn
  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ja

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

From September 2018 to September 2020, patients underwent ankle MRI examination in the Department of Radiology, the Third Hospital of Peking University.

Beskrivelse

Inclusion Criteria:

  1. Without any treatment before imaging examination;
  2. MR of ankle joint was performed within 3 months before operation and the image quality was good;
  3. Arthroscopic operation was performed in our hospital and the operation records were complete.

Exclusion Criteria:

  1. history of ankle surgery, history of cancer or previous fractures.
  2. Unclear image, serious artifact or incomplete clinical data.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Normal control group-Grade 0
Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.
Diagnostisk test: Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
Ligament injury -Grade 1
Arthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear.
Diagnostisk test: Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
Ligament tear-Grade 2
Arthroscopy of the ankle joint revealed partial or complete loss of ligaments.
Diagnostisk test: Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Tidsramme: 2021.1-2022.3.1
The model of deep learning was obtained for diagnosis and grading of anterior fibular ligament and compared with the doctors of different grades.
2021.1-2022.3.1

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Peking University Third Hospital

Efterforskere

  • Studiestol: huishu Yuan, MD, Peking University Third Hospital

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. januar 2021

Primær færdiggørelse (Forventet)

30. december 2021

Studieafslutning (Forventet)

30. marts 2022

Datoer for studieregistrering

Først indsendt

28. juni 2021

Først indsendt, der opfyldte QC-kriterier

29. juni 2021

Først opslået (Faktiske)

8. juli 2021

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

8. juli 2021

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

29. juni 2021

Sidst verificeret

1. juni 2021

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Andre undersøgelses-id-numre

  • M2020460

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

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