Early Screening and Prediction of Eye Disease

The early disease screening and prevention module in our artificial intelligence system, based on deep learning technology, can quickly process massive medical data, accurately locate the lesion, analyze the condition, provide decision assistance, and improve the efficiency and accuracy of doctors.

The early disease screening and prevention module based on deep learning, annotating data with professional medical knowledge, mining and training a disease classification network, which constitutes a classification model and disease recognition model for different lesions. This module mainly includes Gesture recognition, Fundus photography analysis and Open chromatin regions analysis, can quickly detect a variety of eye diseases that threaten vision, diagnose cancer and prevent blindness, so that patients can prevent the disease from worsening in advance.

nomain-drag-pic1Component Models

  • Gesture recognition
    Gesture recognition technology minimizes redundant touches as much as possible, enabling users to overcome the limitations of the device, achieve natural human-computer interaction, break through and reduce the limitations of traditional contact human-computer interaction technology, meet the multi-faceted operation needs of doctors, and greatly improve medicine user experience to continuously enhance the level of medical treatment.
    Component Models

    Figure 1 Gesture Recognition Pipeline (nickgillian.com)

  • Fundus photography analysis
    Through AI fundus screening, a large number of fundus diseases can be assisted in judgment. It can not only reflect a series of ocular fundus diseases such as diabetic retinopathy, glaucoma, and age-related macular disease, but also reflect the disease and accumulation of chronic diseases such as hypertension and diabetes.
    Component Models

    Figure 2 Flowchart of the ensemble-based system for retinal image analysis (Renátó Besenczi, et al. 2016)

  • Open chromatin regions analysis
    Open chromatin regions analysis uses deep learning techniques in artificial intelligence, combined with variational autoencoders and Gaussian mixture models, to extract hidden layer features of single-cell data, projecting problems from complex and sparse high-dimensional chromatin open map space to simple abstraction Low-latitude feature space. This processing can not only find and analyze cell-specific chromatin pattern, but also fill in missing values caused by technical limitations through the sharing of similar cell information, thus cleverly solving the high dimensionality, sparsity, and binarization in single-cell data and other issues.
    Component Models

    Figure 3 Overview of the SCALE framework (Xiong L, Xu K, Tian K, et al. 2019)

AdvantagesAdvantages

  • Accurate
    The core algorithm used in the early disease screening and prevention module has a disease recognition accuracy rate of more than 90%, which is superior to the average level of doctor recognition
  • Fast
    Using high-performance servers and optimal algorithms, the early disease screening and prevention module can process massive amounts of medical data in a short time and provide real-time feedback
  • Efficient
    The early disease screening and prevention module can provide a professional reference for doctors during the diagnosis process, which greatly improves the accuracy of doctors’ diagnosis accuracy and saves more time, which can improve the efficiency of people’s medical treatment.

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