Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
29. juni 2021 oppdatert av: 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.
Studieoversikt
Status
Rekruttering
Forhold
Intervensjon / Behandling
Detaljert beskrivelse
- 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.
- 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.
Studietype
Observasjonsmessig
Registrering (Forventet)
1000
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiekontakt
- Navn: huishu Yuan, MD
- Telefonnummer: 15810245738
- E-post: huishuy@bjmu.edu.cn
Studer Kontakt Backup
- Navn: Ming Ni, MD
- Telefonnummer: 13884794867
- E-post: sdyingxiang2017@163.com
Studiesteder
-
-
Beijing
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Beijing, Beijing, Kina, 010
- Rekruttering
- Peking University Third Hospital
-
Ta kontakt med:
- Huishu Yuan, Dr
- E-post: huishuy@bjmu.edu.cn
-
-
Deltakelseskriterier
Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Barn
- Voksen
- Eldre voksen
Tar imot friske frivillige
Ja
Kjønn som er kvalifisert for studier
Alle
Prøvetakingsmetode
Ikke-sannsynlighetsprøve
Studiepopulasjon
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:
- Without any treatment before imaging examination;
- MR of ankle joint was performed within 3 months before operation and the image quality was good;
- Arthroscopic operation was performed in our hospital and the operation records were complete.
Exclusion Criteria:
- history of ankle surgery, history of cancer or previous fractures.
- Unclear image, serious artifact or incomplete clinical data.
Studieplan
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
Kohorter og intervensjoner
Gruppe / Kohort |
Intervensjon / Behandling |
|---|---|
|
Normal control group-Grade 0
Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.
|
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.
|
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.
|
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
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
|
Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Sponsor
Etterforskere
- Studiestol: huishu Yuan, MD, Peking University Third Hospital
Studierekorddatoer
Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.
Studer hoveddatoer
Studiestart (Faktiske)
1. januar 2021
Primær fullføring (Forventet)
30. desember 2021
Studiet fullført (Forventet)
30. mars 2022
Datoer for studieregistrering
Først innsendt
28. juni 2021
Først innsendt som oppfylte QC-kriteriene
29. juni 2021
Først lagt ut (Faktiske)
8. juli 2021
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
8. juli 2021
Siste oppdatering sendt inn som oppfylte QC-kriteriene
29. juni 2021
Sist bekreftet
1. juni 2021
Mer informasjon
Begreper knyttet til denne studien
Andre studie-ID-numre
- M2020460
Plan for individuelle deltakerdata (IPD)
Planlegger du å dele individuelle deltakerdata (IPD)?
NEI
Legemiddel- og utstyrsinformasjon, studiedokumenter
Studerer et amerikansk FDA-regulert medikamentprodukt
Nei
Studerer et amerikansk FDA-regulert enhetsprodukt
Nei
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