Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

Journal: Computer methods and programs in biomedicine
PMID: 39213898

Abstract

OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups.

Authors

  • Jin Wei
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
  • Yupeng Xu
  • Hanying Wang
    School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China.
  • Tian Niu
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
  • Yan Jiang
    Department of Nursing/Evidence-based Nursing Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yinchen Shen
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
  • Li Su
    China-UK Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China.
  • Tianyu Dou
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
  • Yige Peng
  • Lei Bi
  • Xun Xu
    BGI-Shenzhen, Shenzhen 518083, China.
  • Yufan Wang
    Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai, China.
  • Kun Liu
    Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.

Keywords

External Resources

Popular Topics
Recent Journals