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Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application

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ClinicalTrials.gov Identifier: NCT03899623
Recruitment Status : Recruiting
First Posted : April 2, 2019
Last Update Posted : April 2, 2019
Sponsor:
Information provided by (Responsible Party):
Jin Yuan, Zhongshan Ophthalmic Center, Sun Yat-sen University

Brief Summary:
This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diabetes patients without ocular complications, and 1,000 with diabetic ocular complications. Statistical analysis of the diagnostic sensitivity and specificity of the artificial intelligence system will be made, and ROC curve wil be draw.

Condition or disease
Diabete Mellitus Diabetic Retinopathy

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Study Type : Observational
Estimated Enrollment : 3000 participants
Observational Model: Cohort
Time Perspective: Cross-Sectional
Official Title: Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application
Actual Study Start Date : April 23, 2018
Estimated Primary Completion Date : December 31, 2019
Estimated Study Completion Date : December 31, 2019

Resource links provided by the National Library of Medicine





Primary Outcome Measures :
  1. The diagnostic sensitivity, specificity and ROC curve of the artificial intelligence(AI) system compared with reference standard (ophthalmologist). [ Time Frame: It will take 15~20min for each subject to take the exams. ]
    Sensitivity: the percentage of diabetic retinopathy(DR) patients who are correctly identified as having the condition by AI. Sensitivity=True positive/(True positive+False negative). Specificity: the percentage of healthy people who are correctly identified as not having the condition by AI. Specificity=True negative /(True negative +False positive). The ROC curve was plotted with true positive rate (sensitivity) as the ordinate and false positive rate (1-specificity) as the abscissa.



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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Stratified sampling
Criteria

Inclusion Criteria:

Group A(normal individuals): Meet all the items 1 ~ 5 below

  1. No familial or hereditary retinopathy;
  2. No history of ocular trauma and / or history of ocular disease (except for mild to moderate refractive errors and / or age-related cataracts);
  3. No drug use history that may cause side effects of retina (such as chloroquine, hydroxychloroquine, chlorpromazine, rifampin, etc.); 4 binocular diopter ≤ ± 6.00D; No systemic diseases that may affect the retina.

Group B(diabetes patients without ocular complications): meet any of 1 to 3, and both 4 to 5 items

  1. Diagnosed with type 1 diabetes for more than 5 years;
  2. Diagnosed with type 2 diabetes patients;
  3. Diagnosis of gestational diabetes patients;
  4. Without systemic disease that may affect the retina except for diabetes;
  5. Meet the standards of 1 to 4 items.

Group C(patients with diabetic ocular complications): meet with any of 1 to 3, and all 4 to 6 items

  1. Diagnosed with type 1 diabetes for more than 5 years;
  2. Diagnosed with type 2 diabetes patients;
  3. Diagnosis of gestational diabetes patients;
  4. Meet the guidelines for diagnosis and treatment of diabetic retinopathy in any stage of diabetic retinopathy and / or diabetic macular edema.
  5. Without systemic disease that may affect the retina except for diabetes;
  6. A group to meet the criteria for 1 to 4 items.

Exclusion Criteria:

  1. Refractive pathway is turbid, so that fundus image can not be clearly photographed;
  2. With other fundus diseases and / or other diseases that seriously affect the visual function;
  3. Any eye infection, such as conjunctivitis, keratitis, scleritis, endophthalmitis, acute and chronic dacryocystitis, or eye injury;
  4. Serious systemic diseases such as diabetes, hypertension, heart failur,renal failure;
  5. Can not cooperate with the examiner due to mental or other reasons.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03899623


Contacts
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Contact: Yuan Jin, Professor 8602087333391 yuanjincornea@126.com

Locations
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China, Guangdong
Zhongshan Opthalmic Center Recruiting
Guangzhou, Guangdong, China, 510000
Contact: Yuan Jin, Professor    8602087333391    yuanjincornea@126.com   
Sponsors and Collaborators
Zhongshan Ophthalmic Center, Sun Yat-sen University
Investigators
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Principal Investigator: Yuan Jin, Professor Zhongshan Opthalmic Center

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Responsible Party: Jin Yuan, Principal Investigator, Zhongshan Ophthalmic Center, Sun Yat-sen University
ClinicalTrials.gov Identifier: NCT03899623     History of Changes
Other Study ID Numbers: 2017KYPJ104
First Posted: April 2, 2019    Key Record Dates
Last Update Posted: April 2, 2019
Last Verified: March 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Jin Yuan, Zhongshan Ophthalmic Center, Sun Yat-sen University:
Diabete Mellitus
Diabetic Retinopathy
Artificial Intelligence

Additional relevant MeSH terms:
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Retinal Diseases
Diabetic Retinopathy
Diabetes Mellitus
Eye Diseases
Diabetic Angiopathies
Vascular Diseases
Cardiovascular Diseases
Diabetes Complications
Endocrine System Diseases
Glucose Metabolism Disorders
Metabolic Diseases