Clinical trial NCT03903042: Comparison of Aurora Fundus Camera With Traditional Camera in Diabetic Retinopathy With Visual Artificial Intelligence
Official title: Comparison of Aurora Fundus Camera With Traditional Fundus Camera in Diabetic Retinopathy With Phoebus Visual Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Diabetic Retinopathy
Keywords: Aurora fundus camera
Artificial intelligence
Diabetic retinopathy
Lead sponsor: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2019-04-04
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who were diagnosed with diabetes, more than 18 years of age, male or female Chinese patients.

Brief summary: This study aims to compare the effect of Aurora handheld fundus camera with traditional desktop fundus camera in the fundus photography screening of diabetic patients, and to evaluate the effect of artificial intelligence algorithm in the diagnosis of diabetic retinopathy.

Detailed description: Aurora handheld fundus cameras are used to take fundus photography on diabetic patients in 3 ophthalmic diabetic retinopathy screening centers in China to compare its effect with the hospital's traditional desktop fundus camera, and evaluate the auxiliary diagnostic effect of Phoebus artificial intelligence algorithm in diabetic retinopathy.

Eligibility criteria: Inclusion Criteria:

1. Participants are more than 18 years of age, male or female Chinese patients;
2. Diagnosed with diabetes;
3. Prior written informed consent should be obtained

Exclusion Criteria:

1. Patients with invisible fundus caused by any cause;
2. Patients or his/her licensor unwill to sign an informed consent or follow this protocol;
3. Pregnant women

Primary outcomes:
measure: Image Quality of Aurora camera
timeFrame: within 3 months
Secondary outcomes:
measure: Outcome of gold standard
timeFrame: within 3 months
measure: Image of Aurora camera
timeFrame: 1 month
measure: Image of traditional camera (Center 1: Canon)
timeFrame: 1 month
measure: Image of traditional camera (Center 2: Zeiss)
timeFrame: 1 month
measure: Image of traditional camera (Center 3: Topcon)
timeFrame: 1 month
measure: Outcome of artificial intelligence algorithm
timeFrame: 3 months
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
state: Shanghai
Study officials:
affiliation: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
name: Fenghua Wang
role: PRINCIPAL_INVESTIGATOR
References:
citation: Jin G, Xiao W, Ding X, Xu X, An L, Congdon N, Zhao J, He M. Prevalence of and Risk Factors for Diabetic Retinopathy in a Rural Chinese Population: The Yangxi Eye Study. Invest Ophthalmol Vis Sci. 2018 Oct 1;59(12):5067-5073. doi: 10.1167/iovs.18-24280.
citation: Zheng X, Zhang L. A study of retinopathy analysis in type 2 diabetes patients in Chinese population. Pak J Pharm Sci. 2018 Sep;31(5(Supplementary)):2041-2046.
citation: Hendrick AM, Gibson MV, Kulshreshtha A. Diabetic Retinopathy. Prim Care. 2015 Sep;42(3):451-64. doi: 10.1016/j.pop.2015.05.005.
citation: Wong TY, Bressler NM. Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening. JAMA. 2016 Dec 13;316(22):2366-2367. doi: 10.1001/jama.2016.17563. No abstract available.
citation: Abramoff MD, Niemeijer M, Russell SR. Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices. 2010 Mar;7(2):287-96. doi: 10.1586/erd.09.76.
citation: Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1.