{
  "abstract": "Purpose To generate fundus photographs of multiple kinds of retinal disease, bypassing the requirement of coding technique.Methods The dataset contained fundus photographs of 10 categories of retinal conditions, with 500 fundus photographs in each category. We randomly divided the collected data into a training set (80%) and a test set (20%). Google Colaboratory was used to implement Pix2Pix to generate fundus photographs for each category. We compared the diagnostic abilities of ophthalmologists on both real and synthetic images. The diagnostic performance of the classification models trained on real, synthetic and combined data sets was also compared. Furthermore, the real and synthesised images were distinguished by ophthalmologists and artificial intelligence (AI) image detection websites.Results Fundus photographs of 10 categories were successfully synthesised using our method. The synthetic images showed slightly higher diagnostic accuracy by the three ophthalmologists than the real images (99.7% vs 98.7%, 98.0% vs 96.0% and 99.7% vs 94.3%; p=0.109). Training ResNet-50 and VGG-19 models with a combination of real and synthetic images resulted in significant improvements in accuracy, achieving 93.7% and 89.3%, respectively. Five residents achieved at least 92.5% accuracy in discriminating between real and synthetic images. In contrast, three AI image detection websites showed limited capability in this task, with a maximum accuracy of 51.2%.Conclusion Pix2Pix on Google Colaboratory can efficiently produce a diverse range of fundus images with typical characters.",
  "authors": [
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Huiyu Liang"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Qi Zhang"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Tian Lin"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China",
        "Shenzhen Eye Hospital, Shenzhen, China"
      ],
      "name": "Chenli Hu"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Chaoxin Zheng"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Xue Yao"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Man Chen"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Yifan Chen"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore",
        "Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore",
        "Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore"
      ],
      "name": "Yih Chung Tham"
    },
    {
      "affiliations": [
        "Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China"
      ],
      "name": "Haoyu Chen"
    }
  ],
  "title": "Generation of multidisease fundus photographs with code-free platform",
  "uid": "4247b8d6-297c-512c-82c4-d16300418e6f"
}