Explainable Ocular Fundus Diseases Report Generation System

Image, Body Clinical Trial

Official title:

Explainable Multimodal Deep Neural Networks for Identifying Ocular Fundus Diseases and Report Generation

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05622565
• Other study ID #: 2021KYPJ164
• Status: Recruiting
• Start date: January 2011
• Est. completion date: July 2024

Study information

• Verified date: July 2023
• Source: Sun Yat-sen University
• Contact: Yingfeng Zheng, M.D. Ph.D
• Phone: +8613922286455
• Email: zhyfeng[@]mail.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 15000
• Est. completion date: July 2024
• Est. primary completion date: December 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Eye Diseases
• Image, Body
• Ophthalmological Disorder

Intervention

Diagnostic Test:
• Various modalities of ocular fundus imaging
Through various modalities of ocular fundus imaging, combining with clinical data and the experience of clinicians to diagnose different fundus diseases.

Locations

Country	Name	City	State
China	Zhognshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area under the receiver operating characteristic curve of the deep learning system	The investigators will calculate the area under the receiver operating characteristic curve of the deep learning system and compare this index with human ophthalmologists.	Baseline
Secondary	Intersection-Over-Union of the models' explanation accuracy	The investigators will calculate the Intersection-Over-Union (IOU) (or Jaccard similarity) between the lesion-image attention mapping regions and ground truth regions of the deep learning system.	Baseline
Secondary	Sensitivity and Specificity of the deep learning system	The investigators will calculate the sensitivity and specificity of the deep learning system.	Baseline