Clinical trial NCT05622565: Explainable Ocular Fundus Diseases Report Generation System
Official title: Explainable Multimodal Deep Neural Networks for Identifying Ocular Fundus Diseases and Report Generation
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Ophthalmological Disorder
Image, Body
Keywords: Retinal disease
Choroidal disease
Deep learning
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
First posted: 2022-11-18
Enrollment: 15000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: OTHER
Sampling method: PROBABILITY_SAMPLE
Study population: Ocular fundus images were collected from different health care institutes all over China and from other countries.

Brief summary: To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports.

Detailed description: The ocular fundus is the only part of the human body that can directly see the blood vessel microcirculation and nerve tissue. Through various imaging tests, including Color Fundus Photograph (CFP), Optical Coherence Tomography (OCT), Fluorescein Fundus Angiography (FFA) and Indocyanine Green Angiography (ICGA), etc., it is possible to statically overview or dynamically observe the retina and choroid, the condition of blood vessels and nerves, and comprehensive diagnosis of the disease. The screening, interpreting and accurate diagnosis of ocular fundus diseases are crucial for disease prevention, control and precise treatment. However, due to the variety of fundus examination methods, and the complexity and professionalism of the examination, there is a lack of fundus specialists who have sufficient clinical experience and knowledge to interpret fundus examinations. With the continuous development of artificial intelligence (AI) in diagnosing fundus diseases, various modalities of imaging examination methods are gradually applied to the development of fundus disease diagnosis systems. Moreover, medical images often come with corresponding reports, which are mostly generated by clinicians' or radiologists' experience.

Here, we are establishing a fundus disease diagnosis and report-generating system based on cross-modal ocular fundus imaging examinations, and fundus lesions were visualized at the same time. Multi-center data verification will also be conducted. The results of the research will assist in fundus lesions diagnosis and imaging reports generation. We hope this could popularize more complex fundus imaging examination methods to society, and help improve the early diagnosis and treatment of fundus lesions that cause blindness.

Eligibility criteria: Inclusion Criteria:

* The quality of multimodal ocular fundus disease examination images and corresponding reports should be clinically acceptable.

Exclusion Criteria:

* Reports with key information missing.
* Images with severe image resolution reductions, blur or artifacts were excluded from further analysis.

Primary outcomes:
measure: Area under the receiver operating characteristic curve of the deep learning system
timeFrame: Baseline
Secondary outcomes:
measure: Intersection-Over-Union of the models' explanation accuracy
timeFrame: Baseline
measure: Sensitivity and Specificity of the deep learning system
timeFrame: Baseline
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Zhognshan Ophthalmic Center, Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity,Guangzhou, Guangdong, China, 510060
name: Yingfeng Zheng, M.D. Ph.D
role: PRINCIPAL_INVESTIGATOR
References: