Deep Learning of Knee Joint MRI Intelligent Detection
8. juli 2021 oppdatert av: 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.
Studieoversikt
Status
Rekruttering
Forhold
Detaljert beskrivelse
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
Observasjonsmessig
Registrering (Forventet)
50000
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiekontakt
- Navn: Jia-Kuo Yu
- Telefonnummer: 01082267392
- E-post: yujiakuo@126.com
Studiesteder
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Beijing
-
Beijing, Beijing, Kina, 100191
- Rekruttering
- Institute of Sports Medicine, Peking University Third Hospital
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Ta kontakt med:
- Ai-Bing Huang, PhD
- Telefonnummer: 8615650715003
- E-post: hab165@163.com
-
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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
All patients related to sports injuries
Beskrivelse
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.
Studieplan
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
- Observasjonsmodeller: Kohort
- Tidsperspektiver: Retrospektiv
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Marking system design based on Magnetic Resonance Imaging（MRI）
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
|
Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Sponsor
Samarbeidspartnere
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)
31. desember 2021
Studiet fullført (Forventet)
15. mai 2022
Datoer for studieregistrering
Først innsendt
27. juni 2021
Først innsendt som oppfylte QC-kriteriene
8. juli 2021
Først lagt ut (Faktiske)
12. juli 2021
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
12. juli 2021
Siste oppdatering sendt inn som oppfylte QC-kriteriene
8. juli 2021
Sist bekreftet
1. juni 2021
Mer informasjon
Begreper knyttet til denne studien
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- M2020243
Legemiddel- og utstyrsinformasjon, studiedokumenter
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Nei
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