Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
25. januar 2022 opdateret af: Haotian Lin, Sun Yat-sen University
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g.
retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
Studieoversigt
Status
Rekruttering
Betingelser
Intervention / Behandling
Undersøgelsestype
Observationel
Tilmelding (Forventet)
4000
Kontakter og lokationer
Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.
Studiekontakt
- Navn: Haotian Lin, Ph. D
- Telefonnummer: 13802793086
- E-mail: gddlht@aliyun.com
Studiesteder
-
-
Guangdong
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Guangzhou, Guangdong, Kina, 510060
- Rekruttering
- Zhongshan Ophthalmic Center, Sun Yat-sen University
-
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
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
18 år og ældre (Voksen, Ældre voksen)
Tager imod sunde frivillige
Ja
Køn, der er berettiget til at studere
Alle
Prøveudtagningsmetode
Sandsynlighedsprøve
Studiebefolkning
Participants who had slit-lamp, retinal fundus photography and kidney disease tests at the Department of Nephrology, First Affiliated Hospital of Sun Yat-sen University and Medical Centre of Aikang Health Care, Guangzhou, China
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
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
Intervention / 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
|
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
|
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
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
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
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
Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.
Sponsor
Samarbejdspartnere
Efterforskere
- Studiestol: Yizhi Liu, M.D., Ph.D., Zhongshan Ophthalmic Center, Sun Yat-sen University
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Faktiske)
28. august 2021
Primær færdiggørelse (Forventet)
1. december 2022
Studieafslutning (Forventet)
1. december 2022
Datoer for studieregistrering
Først indsendt
23. januar 2022
Først indsendt, der opfyldte QC-kriterier
25. januar 2022
Først opslået (Faktiske)
4. februar 2022
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
4. februar 2022
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
25. januar 2022
Sidst verificeret
1. januar 2022
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- AIKD-2021
Plan for individuelle deltagerdata (IPD)
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