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SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis

29. juni 2021 oppdatert av: Peking University Third Hospital
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Intervensjon / Behandling

Detaljert beskrivelse

  1. Recognition of labrum images based on LeNet: axial and oblique coronal T2-fs images were used, and all images were corrected and standardized. LeNet identified the images with labrum of the shoulder joint, and the images with labrum structure of shoulder joint were selected from the complete sequence. In contrast, the images without labrum structure were deleted. All the data are divided into a training set (70%, 30% in training set as verification set), and the remaining 30% as a test set to evaluate the accuracy of model recognition. Enter the obtained results into the next step.
  2. Recognition and segmentation of glenoid lip of shoulder joint based on DenseNet: the labrum is recognized by DenseNet in the selected image. The labelimg software based on Python was used to locate the labrum coordinates and then input them into Python for recognition learning. All the data were divided into a training set (70% and 30% of the training set were selected as the verification set). The remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the labrum structure, the labrum structure is locally cut and enlarged to remove the redundant information and improve the recognition efficiency and accuracy. Finally, input the result to the next step.
  3. Recognition and grading of shoulder SLAP injury based on 3D-CNN: recognition and grading of input data through 3D-CNN model. 3D-CNN is divided into eight layers: input layer, hard wire layer H1, convolution layer C2, downsampling layer S3, convolution layer C4, downsampling layer S5, convolution layer C6 and output layer. 3D-CNN constructs a cube by stacking multiple consecutive frames and then uses a 3D convolution kernel in the cube. Through this structure, the feature images in the convolution layer will be connected with multiple adjacent frames in the previous layer to realize the information acquisition of continuous images. Similarly, the data is divided into a training set (70%, and then 30% of the training set is selected as the verification set), and the remaining 30% is used as the test set to evaluate the classification accuracy to identify whether there is labrum injury and grade the image with injury.
  4. Establish CNN combined model: after establishing the model for the axial and oblique coronal view according to the above process (1-3), according to the output characteristics of the CNN classification model, predict the probability of different grades before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combined model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final joint model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the joint model.

Studietype

Observasjonsmessig

Registrering (Forventet)

800

Kontakter og plasseringer

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Studiekontakt

Studer Kontakt Backup

Studiesteder

  • Kina
    • Beijing
      • Beijing, Beijing, Kina, 010
        • Peking University Third Hospital

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

N/A

Kjønn som er kvalifisert for studier

Alle

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.

Beskrivelse

Inclusion Criteria:

  1. Without any treatment before imaging examination;
  2. MR of the shoulder joint was performed within 3 months before the 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 shoulder surgery, tumor, or previous fracture;
  2. 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

  • Observasjonsmodeller: Case-Control
  • Tidsperspektiver: Retrospektiv

Kohorter og intervensjoner

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

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
Tidsramme: 2021.10.1-2022.7.1
The model of deep learning was obtained for diagnosis and grading of SLAP injury and compared with the radiologists of different stages.
2021.10.1-2022.7.1

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

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 (Forventet)

1. oktober 2021

Primær fullføring (Forventet)

1. juni 2022

Studiet fullført (Forventet)

1. juli 2022

Datoer for studieregistrering

Først innsendt

29. juni 2021

Først innsendt som oppfylte QC-kriteriene

29. juni 2021

Først lagt ut (Faktiske)

7. juli 2021

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

7. 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

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • M2020458

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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