Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
Studieoversigt
Status
Status
Betingelser
Betingelser
Intervention / Behandling
Intervention / Behandling
Undersøgelsestype
Undersøgelsestype
Tilmelding (Forventet)
Tilmelding
Kontakter og lokationer
Studiekontakt
Studiekontakt
- Navn: Haotian Lin, Ph. D
- Telefonnummer: 13802793086
- E-mail: gddlht@aliyun.com
Studiesteder
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Guangdong
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Guangzhou, Guangdong, Kina, 510060
- Rekruttering
- Zhongshan Ophthalmic Center, Sun Yat-sen University
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Kontakt:
- Haotian Lin, M.D., Ph.D
- Telefonnummer: +8613802793086
- E-mail: haot.lin@hotmail.com
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Kontakt:
- Qianni Wu, M.D., Ph.D
- Telefonnummer: +8615521506995
- E-mail: wuqianni@gzzoc.com
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-
Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
Inclusion Criteria:
- Patients previously received kidney biopsy, ophthalmic examinations and routine examinations of the department of nephrology during in-hospital period with BCVA>0.5.
Exclusion Criteria:
- Patients without retinal fundus images or kidney diseases.
- The quality of the retinal fundus images can not meet the requirement for furthur analysis.
- Severe loss of results of routine examinations of the department of nephrology.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
Intervention / BehandlingIntervention / Behandling |
|---|---|
|
Development Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
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The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Development Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
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The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Validation Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Validation Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Test Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Test Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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Area under the receiver operating characteristic curve of the deep learning system
Tidsramme: baseline
|
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors
|
baseline
|
Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Sensitivity and specificity of the deep learning system
Tidsramme: baseline
|
The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors
|
baseline
|
Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Samarbejdspartnere
Samarbejdspartnere
Efterforskere
Efterforskere
- Studiestol: Yizhi Liu, M.D., Ph.D., Zhongshan Ophthalmic Center, Sun Yat-sen University
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Forventet)
Primær færdiggørelse
Studieafslutning (Forventet)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- AIKD-2021
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
Studerer et amerikansk FDA-reguleret lægemiddelprodukt
Studerer et amerikansk FDA-reguleret enhedsprodukt
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