Explainable Ocular Fundus Diseases Report Generation System

Study Overview
=================
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.

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

Conditions
-----------------
Ophthalmological Disorder, Image, Body

Intervention / Treatment
-----------------
* Diagnostic Test: Various modalities of ocular fundus imaging


Participation Criteria
=================
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.
Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training set<br>Multimodal ocular fundus images and corresponding reports collected from multiple screening sites in China. | |
| Internal Validation set<br>Records separated from the training set. | |
| External Test set<br>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<br>* Through various modalities of ocular fundus imaging, combining with clinical data and the experience of clinicians to diagnose different fundus diseases.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| Sensitivity and Specificity of the deep learning system | The investigators will calculate the sensitivity and specificity of the deep learning system. | Baseline | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Retinal disease, Choroidal disease, Deep learning