Visual identification pain grading evaluation method

Abstract

The invention discloses a visual identification pain grading evaluation method, and belongs to the field of pain evaluation. The method comprises the following steps: acquiring image information of a patient; extracting posture information of the patient through a skeleton model based on the posture image, and extracting corresponding positions and relative distance information of five sense organs of the patient through a five sense organ model based on the face image; and inputting the extracted feature information into a trained convolutional neural network and long and short memory network model to obtain a patient pain grading evaluation result. According to the technical scheme provided by the invention, the pain assessment method based on the visual means is realized, wherein the extracted patient data features comprise the bone posture information of the patient and the information of the five sense organs of the human face.

Description

technical field [0001] This application relates to the field of artificial intelligence, in particular to a method for medical image recognition. Background technique [0002] Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. Pain is a subjective and complex perception, and pain intensity is often assessed by self-reported scales in clinical settings. Pain assessment aids decision-making on relevant medical interventions. [0003] Such subjective methods are considered the "gold standard" of pain measurement. However, the reliability of self-reports in some contexts may be affected by a range of physiological, psychological and environmental factors. For example, repeated assessments by many pain patients tend to exacerbate their severity and negative attitudes. [0004] In view of the above-mentioned defects, the creators of the present invention extract the patient's facial expression features and posture features...

AI Technical Summary

Problems solved by technology

However, the reliability of self-reports in some contexts may be affected by a range of physiological, psychological and environmental factors

For example, many pain patients tend to exacerbate their severity and have negative attitudes when they are repeatedly assessed

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