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

29 juni 2021 uppdaterad 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.

Studieöversikt

Status

Har inte rekryterat ännu

Betingelser

Intervention / Behandling

Detaljerad beskrivning

  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.

Studietyp

Observationell

Inskrivning (Förväntat)

800

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studiekontakt

Studera Kontakt Backup

Studieorter

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

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Barn
  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

N/A

Kön som är behöriga för studier

Allt

Testmetod

Icke-sannolikhetsprov

Studera befolkning

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

Beskrivning

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

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Observationsmodeller: Case-Control
  • Tidsperspektiv: Retrospektiv

Kohorter och interventioner

Grupp / Kohort
Intervention / Behandling
Normal control group-Grade 0
Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.
Diagnostiskt 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.
Diagnostiskt 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.
Diagnostiskt test: Diagnositic test
The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.

Vad mäter studien?

Primära resultatmått

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

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

Peking University Third Hospital

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Förväntat)

1 oktober 2021

Primärt slutförande (Förväntat)

1 juni 2022

Avslutad studie (Förväntat)

1 juli 2022

Studieregistreringsdatum

Först inskickad

29 juni 2021

Först inskickad som uppfyllde QC-kriterierna

29 juni 2021

Första postat (Faktisk)

7 juli 2021

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

7 juli 2021

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

29 juni 2021

Senast verifierad

1 juni 2021

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • M2020458

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

Nej

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .

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