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Deep Learning of Knee Joint MRI Intelligent Detection
8 juli 2021 bijgewerkt door: Peking University Third Hospital
Knee joint is the most common part of sports injury.
MRI is a powerful tool to diagnose knee joint injury.
However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed diagnosis.
The emerging deep learning technology can establish automatic recognition model through large samples.
A large sample of knee joint MRI was collected retrospectively to train the deep learning model of knee joint MRI, and the sensitivity and specificity of the deep learning model were verified in multi center.
Depending on the clinical needs, the deep learning model annotation system is established.
A large number of knee MRI were obtained and labeled.
According to the knee joint MRI training depth learning model, and iterative optimization, the final version is formed.
Multi center validation was carried out.
Continuous operation records and corresponding preoperative knee MRI were obtained from multiple hospitals.
The sensitivity and specificity of the model were calculated with operation records as the gold standard.
At the same time, an expert team composed of senior radiologists and sports medicine doctors was organized to read the films.
The sensitivity and specificity of manual reading and AI reading were compared to prove the superiority of AI reading.
This study can improve the efficiency of clinical MRI film reading, reduce the workload of doctors, improve the film reading level of grass-roots hospitals, promote the development of the discipline, and has good social benefits and market prospects.
Studie Overzicht
Toestand
Werving
Conditie
Gedetailleerde beschrijving
The knee joint is the most common sports injury site in the human body, including ligament rupture, meniscus tear, cartilage lesions, and free body formation.
Knee MRI has extremely high sensitivity and specificity in diagnosing knee diseases, especially its negative predictive value is close to 100%, and it is an effective means to assist clinicians in diagnosing knee diseases.
However, there are many MRI sequences of the knee joint, and different diseases have different imaging effects on various sequences, and the types of knee joint diseases are complicated, so it takes a long time to evaluate the knee joint MRI.
Due to the huge clinical demand for knee MRI, it has caused a great burden on radiology and sports medicine orthopedics.
At the same time, for some special injuries of the knee joint, such as hidden meniscus tear, rupture of the anterior cross part and adhesion in place after rupture, local ligament injury, etc., the conclusions given by different readers are very different, and it is easy to miss the diagnosis.
And the missed diagnosis seriously affects the prognosis of the knee joint, leading to the progression of arthritis.
In addition, professional musculoskeletal system imaging experts have a long training cycle, and a large number of orthopedic doctors and radiologists in basic hospitals have limited reading skills for knee MRI, which limits the development of local sports medicine disciplines and the development of related diagnosis and treatment.
The purpose of our research is to train the deep learning model of knee MRI through multi-center and large sample of knee MRI; Multi-center verification of the sensitivity and specificity of the knee MRI deep learning model, and compare the accuracy of the deep learning model and manual image reading.
Studietype
Observationeel
Inschrijving (Verwacht)
50000
Contacten en locaties
In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.
Studiecontact
- Naam: Jia-Kuo Yu
- Telefoonnummer: 01082267392
- E-mail: yujiakuo@126.com
Studie Locaties
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Beijing
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Beijing, Beijing, China, 100191
- Werving
- Institute of Sports Medicine, Peking University Third Hospital
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Contact:
- Ai-Bing Huang, PhD
- Telefoonnummer: 8615650715003
- E-mail: hab165@163.com
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Deelname Criteria
Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
- Kind
- Volwassen
- Oudere volwassene
Accepteert gezonde vrijwilligers
NVT
Geslachten die in aanmerking komen voor studie
Allemaal
Bemonsteringsmethode
Niet-waarschijnlijkheidssteekproef
Studie Bevolking
All patients related to sports injuries
Beschrijving
Inclusion Criteria:
- ACL-injured patients;
- Follow-up of patients after ACL injury;
- patients with genetic predisposition to ACL injury;
Exclusion Criteria:
- Patients with joint injury caused by clear external forces;
- Definitely have stroke, heart disease, epilepsy, cranial neurosurgery, migraine;
- Have had a concussion or head injury in the past 6 months.
Studie plan
Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.
Hoe is de studie opgezet?
Ontwerpdetails
- Observatiemodellen: Cohort
- Tijdsperspectieven: Retrospectief
Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
---|---|---|
Marking system design based on Magnetic Resonance Imaging(MRI)
Tijdsspanne: 2021
|
According to the development goals, combined with the performance of MRI and the structure of the model algorithm, the labeling rules and logic of knee MRI are determined.
On this basis, a labeling system is designed, and different labeling tools are designed for a variety of lesions.
|
2021
|
Data export and annotation
Tijdsspanne: 2021
|
Encrypt the MRI file and import it into the medical standard intelligent labeling system.
Create a dedicated tagging account for each tagger to tag.
Based on the previously marked image data, develop algorithms for segmenting different lesion areas.
|
2021
|
Build a deep learning model
Tijdsspanne: 2021
|
According to the diagnostic logic, we select the coronal and sagittal images of the knee joint T2 MRI sequence for analysis.
And choose the Resnext model that has been verified by a large number of ImageNet and other large data sets to extract the features of the coronal out-of-state images.
After the multi-layer convolution operation, the key feature representation of the image is extracted.
At the same time, in the process of feature extraction, the batch normalization module is used to perform feature transformation to highlight the most meaningful part of the feature.
|
2021
|
Medewerkers en onderzoekers
Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.
Sponsor
Medewerkers
Studie record data
Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.
Bestudeer belangrijke data
Studie start (Werkelijk)
1 januari 2021
Primaire voltooiing (Verwacht)
31 december 2021
Studie voltooiing (Verwacht)
15 mei 2022
Studieregistratiedata
Eerst ingediend
27 juni 2021
Eerst ingediend dat voldeed aan de QC-criteria
8 juli 2021
Eerst geplaatst (Werkelijk)
12 juli 2021
Updates van studierecords
Laatste update geplaatst (Werkelijk)
12 juli 2021
Laatste update ingediend die voldeed aan QC-criteria
8 juli 2021
Laatst geverifieerd
1 juni 2021
Meer informatie
Termen gerelateerd aan deze studie
Aanvullende relevante MeSH-voorwaarden
Andere studie-ID-nummers
- M2020243
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Nee
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
Nee
Deze informatie is zonder wijzigingen rechtstreeks van de website clinicaltrials.gov gehaald. Als u verzoeken heeft om uw onderzoeksgegevens te wijzigen, te verwijderen of bij te werken, neem dan contact op met register@clinicaltrials.gov. Zodra er een wijziging wordt doorgevoerd op clinicaltrials.gov, wordt deze ook automatisch bijgewerkt op onze website .
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