Visualization method and device for fundus abnormality
By automatically identifying and classifying fundus images, and generating differentiated visualization results, the problem of unclear diagnosis of fundus abnormalities in existing technologies is solved, achieving higher diagnostic accuracy and better doctor-patient communication.
CN121961995APending Publication Date: 2026-05-01EVISION TECH (BEIJING) CO LTD
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Patent Information
- Application Number
- CN202511829492.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-05-01
AI Technical Summary
Technical Problem
Existing technologies cannot effectively identify and label fundus abnormalities, especially when multiple abnormalities coexist, leading to unclear diagnostic results and significant discrepancies.
Method used
This paper provides a visualization method for fundus abnormalities. By automatically identifying and classifying target fundus images, it generates differentiated visualization results, including information such as the type and severity of the abnormality.
Benefits of technology
It has improved the accuracy of diagnosing fundus abnormalities, reduced errors caused by human factors, enhanced the effectiveness of doctor-patient communication, simplified the diagnostic process, and improved work efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides a visualization method and device for fundus abnormality. One embodiment of the method comprises the following steps: firstly, carrying out identification processing on an abnormal region in a target fundus image to obtain an abnormal classification result; and secondly, on the basis of the anomaly classification result, performing differential display on all the anomaly regions in the target fundus image according to anomaly types, and generating a fundus anomaly type visualization result. Therefore, by automatically identifying all the fundus abnormalities, errors caused by human factors are reduced, and the accuracy of fundus abnormality diagnosis is improved; according to the method, the recognized anomaly classification result is visually displayed, a doctor can be helped to more visually explain the fundus anomaly condition to a patient, the patient can better understand the illness state of the patient, and the effectiveness of doctor-patient communication is enhanced.
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