Explainable Ocular Fundus Diseases Report Generation System
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Training set Multimodal ocular fundus images and corresponding reports collected from multiple screening sites in China. |
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| Internal Validation set Records separated from the training set. |
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| External Test set Multimodal ocular fundus images and corresponding reports collected from multi-centers in China and around the world. |
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.
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Outcome Measures
Primary Outcome Measures
- Area under the receiver operating characteristic curve of the deep learning system [Baseline]
The investigators will calculate the area under the receiver operating characteristic curve of the deep learning system and compare this index with human ophthalmologists.
Secondary Outcome Measures
- Intersection-Over-Union of the models' explanation accuracy [Baseline]
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.
- Sensitivity and Specificity of the deep learning system [Baseline]
The investigators will calculate the sensitivity and specificity of the deep learning system.
Eligibility Criteria
Criteria
Inclusion Criteria:
- The quality of multimodal ocular fundus disease examination images and corresponding reports should be clinically acceptable.
Exclusion Criteria:
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Reports with key information missing.
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Images with severe image resolution reductions, blur or artifacts were excluded from further analysis.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Zhognshan Ophthalmic Center, Sun Yat-sen University | Guangzhou | Guangdong | China | 510000 |
Sponsors and Collaborators
- Sun Yat-sen University
Investigators
- Principal Investigator: Yingfeng Zheng, M.D. Ph.D, Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity,Guangzhou, Guangdong, China, 510060
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2021KYPJ164