Clinical trial NCT03476291: Research of Automated Maculopathy Screening Based on AI Techniques Using OCT Images
Official title: Research of Automated Maculopathy Screening by Optical Coherent Tomography Image-based Deep Learning Techniques
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Maculopathy
Keywords: macular diseases
screening
OCT
Deep learning
AI
Lead sponsor: The First Affiliated Hospital with Nanjing Medical University
Sponsor class: OTHER
First posted: 2018-03-26
Enrollment: 20000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: no age limited, no gender-based

Brief summary: The investigators expect to develop an algorithm that can interpret OCT images and automated determine whether the macula is normal or not by using OCT image-based deep learning techniques. And investigators wish to develop software applications that will help better screen and diagnose macular diseases in resource-limited areas.

Detailed description: The investigators will apply deep learning convolutional neural network by using ImageNet for an automated detection of multiple retinal diseases with OCT horizontal B-scans with a high-quality labeled database. Datasets, including training dataset, testing dataset and validation datasets, will be built by ophthalmologists of the First affiliated hospital of Nanjing Medical University according to the standardized annotation guidelines.

Eligibility criteria: Inclusion Criteria:

* All patients attending the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University within 5 years and who received known, clear diagnoses with digital retinal imaging (including OCT, fundus digital photographs and fundus fluorescein angiography, at least with OCT images) as part of their routine clinical care, will be eligible for inclusion in this study.

Exclusion Criteria:

* Hardcopy examinations (i.e., photos of paper reports of OCT imaging performed at other hospitals) will be ineligible.
* Data from patients who have previously manually requested that their data should not be shared, even for research purposes in anonymised form, and have informed the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University of this desire (even in previously conducted studies or other on-going studies in this hospital), will be excluded, and their data will not be upload to the cloud platform before research begins.
* Data from eyes tamponed with silicone oil or gas (i.e., C3F8) will be ineligible.
* Data with poor image quality, such as incomplete images, inverted images, blurred or cracked images and images with a very weak signal (i.e., vitreous haemorrhage), will be ineligible.

Primary outcomes:
measure: receiver operating characteristic(ROC) curve of the algorithm
timeFrame: approximately 1 year
measure: Area under the ROC curve(AUC)
timeFrame: approximately 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Nanjing
country: China
facility: The First Affiliated Hospital with Nanjing Medical University
state: Jiangsu
Study officials:
affiliation: The First Affiliated Hospital with Nanjing Medical University
name: Songtao Yuan, doctor
role: PRINCIPAL_INVESTIGATOR
References: